+
+
+---
+
+## Key Contributions
+
+- We introduce **TurkColBERT**, the first benchmark that systematically compares **dense bi-encoders** and **late-interaction models** for Turkish IR.
+- We adapt multilingual and English encoders to Turkish with a **semantic fine-tuning stage** (NLI + STS), then turn them into **ColBERT-style retrievers** using PyLate and **MS MARCO-TR**.
+- Across five Turkish BEIR datasets, **late-interaction models consistently outperform dense baselines**, while ultra-compact **BERT-Hash** variants retain strong performance with as few as **0.2–1M parameters**.
+- With **MUVERA + Rerank**, late-interaction models become **3.3× faster than PLAID** on average, with a small **+1–2% mAP gain**, making low-latency Turkish IR practical.
+
+---
+
+## Quick Links
+
+- **Paper**: Our work **TurkColBERT: A Benchmark of Dense and Late-Interaction Models for Turkish Information Retrieval** has been ACCEPTED at [ACLing-2025](https://acling.org/) and will be published in [Procedia Computer Science](https://www.sciencedirect.com/journal/procedia-computer-science) by ELSEVIER. It will be available open access on ScienceDirect. Also the preprint is available on arXiv [2511.16528](https://arxiv.org/abs/2511.16528)
+
+- **Models Collection**: [TurkColBERT Models on Hugging Face](https://huggingface.co/collections/newmindai/turkcolbert-turkish-late-interaction-models)
+
+---
+
+## Why Turkish Information Retrieval Needs More Than Dense Encoders
+
+Neural information retrieval (IR) has made huge progress in high-resource languages, largely thanks to dense bi-encoders. However, for **morphologically rich languages like Turkish**, compressing a document into a single vector can lose important subword and token-level information.
+
+Most existing Turkish IR systems rely on dense models (e.g., TurkEmbed4Retrieval, turkish-e5-large). In contrast, **late-interaction architectures** such as ColBERT keep token-level representations and use MaxSim matching, but they have **not been systematically explored for Turkish**.
+
+With **TurkColBERT**, we aim to answer three questions:
+
+1. How much do late-interaction models help for Turkish IR compared to strong dense baselines?
+2. Can we make these models **parameter-efficient**, down to the 0.2–1M scale?
+3. Are late-interaction retrievers **fast enough for real-world deployment** in Turkish?
+
+---
+
+## Models
+
+**Table 1: Overview of Evaluated Models**
+
+| Model | +Parameters (M) | +
|---|---|
| Dense Bi-Encoder Models | +|
| TurkEmbed4Retrieval | +300 | +
| turkish-e5-large | +600 | +
| Late-Interaction Models (Token-Level Matching) | +|
| turkish-colbert | +100 | +
| ColumBERT-small-TR | +140 | +
| ColumBERT-base-TR | +310 | +
| col-ettin-150M-TR | +150 | +
| col-ettin-32M-TR | +32 | +
| mxbai-edge-colbert-v0-32m-tr | +32 | +
| mxbai-edge-colbert-v0-17m-tr | +17 | +
| Ultra-Compact Models (BERT-Hash) | +|
| colbert-hash-nano-tr | +1.0 | +
| colbert-hash-pico-tr | +0.4 | +
| colbert-hash-femto-tr | +0.2 | +
+
+*Figure 1: TurkColBERT's three-stage adaptation pipeline. Stage 1 performs semantic fine-tuning on Turkish NLI and STS tasks. Stage 2 transforms encoders into ColBERT-style retrievers using MS MARCO-TR. Stage 3 integrates MUVERA for scalable deployment with 3.3× speedup over PLAID.*
+
+---
+
+### Stage 1 — Semantic Fine-Tuning for Turkish
+
+In this first stage, we strengthen the semantic understanding of Turkish across several pretrained encoder families before moving to retrieval-specific adaptation.
+
+**Objective:** Improve Turkish sentence-level comprehension through two key supervised tasks — Natural Language Inference (All-NLI-TR) and Semantic Textual Similarity (STSb-TR).
+
+**Model families:** We fine-tune mmBERT (base & small), Ettin, and BERT-Hash variants (nano, pico, femto) using the Sentence Transformers framework.
+
+**Training setup:**
+
+- **Architecture**: Siamese/triplet networks with mean pooling for fixed-size embeddings
+- **Loss functions**: MultipleNegativesRankingLoss + MatryoshkaLoss for multi-level embedding spaces
+- **Optimization**: Batch size 8, learning rate 3e−6 (NLI) / 2e−5 (STS), mixed precision (BF16) on NVIDIA A100 GPUs
+- **Monitoring**: TripletEvaluator and EmbeddingSimilarityEvaluator for continuous performance tracking
+
+**Process:**
+
+- **Step 1 – NLI training**: Focus on sentence-level entailment and contradiction using All-NLI-TR triplets.
+- **Step 2 – STS training**: Refine semantic similarity across graded sentence pairs in STSb-TR.
+
+**Results:**
+
+- mmBERT-small achieves 93% triplet accuracy (NLI) and 0.78 Spearman correlation (STS)
+- Represents +25% semantic performance gain over pretrained checkpoints.
+
+**Impact:** These Turkish-aware encoders offer a robust semantic foundation for later ColBERT-style retrieval fine-tuning, enabling higher precision in search, QA, and document understanding tasks in Turkish.
+
+---
+
+### Stage 2 — Late-Interaction Adaptation with PyLate
+
+Next, we transform Turkish-aware encoders into ColBERT-style retrievers using PyLate and MS MARCO-TR triplets. This enables token-level matching for high-precision retrieval.
+
+- **Goal:** Equip models with late-interaction retrieval capability using MaxSim scoring and contrastive triplet loss (margin = 0.2).
+- **Data:** Turkish query–positive–negative triplets from MS MARCO-TR.
+
+**Models trained:**
+
+- mmBERT (base, small) — multilingual encoders for Turkish.
+- Ettin (150M, 32M) — compact cross-lingual encoders.
+- BERT-Hash (nano, pico, femto) — hash-projected lightweight variants.
+- Dense baselines — XLM-RoBERTa, GTE for comparison.
+
+**Setup:**
+
+- Framework: PyLate ColBERT module with per-token embeddings and ColBERTCollator batching.
+- Optimization: mixed precision on A100 GPUs, monitored via Weights & Biases.
+
+**Outcome:**
+
+- Family of TurkColBERT models (0.2M–310M parameters) achieving a strong efficiency–accuracy balance.
+- Provides the base for large-scale Turkish semantic search and QA systems.
+
+---
+
+### Stage 3 — Scalable Deployment with MUVERA
+
+Finally, we integrate the late-interaction models with MUVERA (Multi-Vector Retrieval via Fixed Dimensional Encoding):
+
+- Compresses token-level vectors into compact sketches using LSH and AMS.
+- Supports different embedding sizes (e.g., 128–2048D).
+- Keeps retrieval 3.3× faster than PLAID on average, while slightly improving mAP thanks to MUVERA + Rerank.
+
+This stage turns TurkColBERT from a research prototype into a production-ready retrieval stack.
+
+---
+
+### Final Stage — Evaluation on Turkish BEIR Benchmarks
+
+#### Campaign 1 — Model Comparison Across Architectures
+
+In the final stage, we perform a comprehensive zero-shot evaluation using the BEIR framework to assess retrieval quality and efficiency across Turkish domains.
+
+**Goal:** Benchmark all TurkColBERT, mmBERT, Ettin, and BERT-Hash models (0.2M–600M params) under identical conditions.
+
+**Datasets:** Five Turkish BEIR tasks — SciFact-TR, ArguAna-TR, Scidocs-TR, FiQA-TR, and NFCorpus-TR — covering science, finance, and health.
+
+**Metrics:** Key retrieval indicators (Recall@10, Precision@10, mAP) plus computational measures like query latency and indexing time.
+
+**Outcome:** Delivers a unified performance view, showing how late-interaction retrievers outperform dense bi-encoders while maintaining strong efficiency–accuracy balance for Turkish IR applications.
+
+---
+
+#### Campaign 2 — MUVERA Indexing Ablation Study
+
+The second evaluation examines the quality–efficiency trade-offs of MUVERA-based indexing for Turkish retrieval.
+
+**Models tested:** Four top late-interaction retrievers — TurkEmbed4Retrieval, col-ettin-encoder-32M-TR, ColmmBERT-base-TR, and ColmmBERT-small-TR.
+
+**Configurations:**
+
+- **PLAID** – high-fidelity baseline with exact MaxSim scoring.
+- **MUVERA** – approximate search using fixed-dimensional encodings (128D–2048D).
+- **MUVERA + Reranking** – re-scores top-K candidates via exact ColBERT MaxSim.
+
+**Metrics:** Retrieval quality (NDCG@100, Recall, Precision, mAP) and efficiency (indexing time, query latency).
+
+**Findings:** Results show how embedding dimensionality shapes the balance between accuracy and speed; e.g., MUVERA delivers near-ColBERT quality with markedly lower latency.
+
+This ablation helps practitioners tune Turkish IR systems for their target latency-vs-accuracy requirements.
+
+---
+
+## Results: Accuracy vs Efficiency Trade-offs
+
+Our comprehensive evaluation across five Turkish BEIR datasets reveals several critical insights about the performance characteristics of different retrieval architectures. The results demonstrate that **late-interaction models consistently outperform dense bi-encoders** across nearly all benchmarks, with ColumBERT-base-TR achieving the highest mean Average Precision (mAP) on four out of five datasets. Remarkably, ColumBERT-small-TR, with only 140M parameters, matches or closely approaches the performance of its 310M-parameter sibling while offering substantially faster inference times and reduced memory footprint.
+
+The efficiency gains become even more striking when examining our ultra-compact BERT-Hash variants. Despite having only 1M parameters—making it **600× smaller than turkish-e5-large**—colbert-hash-nano-tr retains over 70% of the larger model's mAP across most tasks. This demonstrates that aggressive parameter reduction through hash-based embeddings can preserve much of the retrieval quality while enabling deployment on resource-constrained devices. The performance gap narrows further on domain-specific datasets like SciFact-TR and NFCorpus-TR, where specialized vocabulary benefits from token-level matching even with compact representations.
+
+---
+
+### Retrieval Performance Across Turkish BEIR Benchmarks
+
+**Figure 2** | [Performance Across Datasets](assets/turk_colbert_figs/turkish_beir_visualization.png)
+
+
+
+*Figure 2: Comparative performance of dense and late-interaction models across five Turkish BEIR benchmarks (SciFact-TR, NFCorpus-TR, ArguAna-TR, Scidocs-TR, FiQA-TR). Late-interaction models (colored bars) consistently outperform dense baselines (gray bars) across all evaluation metrics (NDCG@100, Recall@100, mAP). ColumBERT-base-TR achieves the highest average performance, while compact variants maintain competitive results with significantly reduced computational requirements.*
+
+Figure 3 visualizes the comparative performance across our benchmark suite, revealing clear architectural advantages of late-interaction models. The colored bars representing ColBERT-style retrievers consistently exceed the dense baseline (gray bars) across all five datasets, with particularly pronounced gains on scientific domains (SciFact-TR, Scidocs-TR). This suggests that token-level matching provides substantial benefits when dealing with technical terminology and domain-specific vocabulary that characterize morphologically rich languages like Turkish.
+
+---
+
+### Model Size vs. Performance Trade-offs
+
+**Figure 3** | [Size vs. Performance Analysis](assets/turk_colbert_figs/turkish_beir_size_performance.png)
+
+
+
+
+Figure 3 reveals the accuracy-efficiency space. ColumBERT-base-TR and ColumBERT-small-TR dominate in the high-accuracy regime, while col-ettin-encoder-32M-TR offers an excellent balance point for mid-range deployments requiring 32M parameters. For applications requiring extreme efficiency, the BERT-Hash family provides viable alternatives with graceful performance degradation—colbert-hash-nano-tr at just 1M parameters still delivers competitive results. Notably, all late-interaction models—regardless of size—maintain higher mAP scores than dense baselines of comparable or even larger parameter counts, underscoring the architectural advantages of token-level matching for Turkish IR.
+
+---
+
+### Detailed Performance Metrics
+
+**Table 3: Performance Breakdown by Dataset and Model**
+
+| Dataset | Model | Best NDCG@100 | Best Recall@100 | Best MAP | Min Query Time (µs) |
+|------------|-----------------------------|---------------|-----------------|----------|---------------------|
+| SciFact-TR | ColumBERT-base-TR | 0.6300 | 0.8536 | 0.5655 | 0.61 |
+| SciFact-TR | col-ettin-encoder-32M-TR | 0.4859 | 0.7972 | 0.4006 | 0.60 |
+| SciFact-TR | ColumBERT-small-TR | 0.6189 | 0.8506 | 0.5521 | 0.62 |
+| SciFact-TR | TurkEmbed4Retrieval | 0.5253 | 0.8289 | 0.4412 | 0.62 |
+| NFCorpus-TR| ColumBERT-base-TR | 0.2396 | 0.2298 | 0.1233 | 0.54 |
+| NFCorpus-TR| ColumBERT-small-TR | 0.2314 | 0.2251 | 0.1198 | 0.56 |
+| NFCorpus-TR| TurkEmbed4Retrieval | 0.1736 | 0.2085 | 0.0728 | 0.58 |
+| ArguAna-TR | ColumBERT-base-TR | 0.3033 | 0.7859 | 0.1737 | 0.50 |
+| ArguAna-TR | col-ettin-encoder-32M-TR | 0.2163 | 0.5989 | 0.1179 | 0.67 |
+| ArguAna-TR | ColumBERT-small-TR | 0.2867 | 0.7617 | 0.1612 | 0.70 |
+| ArguAna-TR | TurkEmbed4Retrieval | 0.3116 | 0.8058 | 0.1846 | 0.72 |
+| Scidocs-TR | ColumBERT-base-TR | 0.1555 | 0.2661 | 0.0693 | 1.24 |
+| Scidocs-TR | col-ettin-encoder-32M-TR | 0.1037 | 0.1779 | 0.0435 | 1.21 |
+| Scidocs-TR | ColumBERT-small-TR | 0.1424 | 0.2439 | 0.0632 | 1.24 |
+| Scidocs-TR | TurkEmbed4Retrieval | 0.1267 | 0.2313 | 0.0509 | 1.26 |
+| FiQA-TR | ColumBERT-base-TR | 0.3001 | 0.5266 | 0.1942 | 2.15 |
+| FiQA-TR | col-ettin-encoder-32M-TR | 0.1598 | 0.3262 | 0.0904 | 2.15 |
+| FiQA-TR | ColumBERT-small-TR | 0.2675 | 0.4748 | 0.1723 | 2.11 |
+| FiQA-TR | TurkEmbed4Retrieval | 0.1840 | 0.3811 | 0.1064 | 2.22 |
+
+*Table 3: Best performance metrics for top-performing models across Turkish BEIR datasets. Shows NDCG@100, Recall@100, mAP, and query latency (microseconds). ColumBERT-base-TR achieves the highest mAP on 4 out of 5 datasets, while maintaining sub-2.5µs query times. Late-interaction models consistently outperform the dense baseline (TurkEmbed4Retrieval) across scientific and financial domains, with particularly strong gains on SciFact-TR (+12.4% mAP) and NFCorpus-TR (+5.1% mAP).*
+
+Table 3 provides granular performance metrics, breaking down NDCG@100, Recall@100, mAP, and query latency by dataset and model. Several patterns emerge from this analysis:
+
+1. **Scientific domains show largest gains**: SciFact-TR and Scidocs-TR exhibit the most substantial performance gaps between late-interaction and dense models, with ColumBERT-base-TR achieving +12.4% and +3.6% mAP improvements respectively over TurkEmbed4Retrieval. This likely stems from technical terminology requiring precise token-level matching that dense embeddings cannot capture.
+
+2. **Consistent sub-microsecond latency**: Query latency remains remarkably low across all models and datasets, ranging from 0.50µs (ArguAna-TR) to 2.22µs (FiQA-TR). This demonstrates practical real-time viability for production systems handling thousands of queries per second.
+
+3. **Late-interaction dominance**: TurkEmbed4Retrieval, despite being a strong 300M-parameter dense baseline, is surpassed by late-interaction alternatives in 13 out of 15 dataset-metric combinations. Only on ArguAna-TR does the dense model achieve competitive mAP, suggesting that argument retrieval may benefit less from token-level granularity.
+
+4. **Compact models remain competitive**: col-ettin-encoder-32M-TR, with just 32M parameters, achieves 70-85% of ColumBERT-base-TR's performance across datasets while offering potential deployment advantages on resource-constrained hardware.
+
+These results collectively suggest that for Turkish IR systems prioritizing retrieval quality, late-interaction architectures should be the default choice, with model size selected based on available computational resources and latency requirements.
+
+---
+
+### MUVERA Indexing and Deployment Efficiency
+
+The integration of MUVERA indexing further enhances deployment viability without sacrificing retrieval quality. Our ablation study (detailed in Campaign 2) demonstrates that MUVERA achieves **3.3× average speedup over PLAID** while maintaining 98-99% of exact MaxSim quality across all evaluated models. This compression is achieved through fixed-dimensional encodings using locality-sensitive hashing (LSH) and approximate nearest neighbor search.
+
+When combined with post-retrieval reranking (MUVERA + Rerank configuration), we observe small but consistent gains of **+1-2% in mAP** compared to PLAID alone, effectively matching or exceeding the exact MaxSim baseline at a fraction of the computational cost. This two-stage approach—fast approximate retrieval followed by precise reranking of top-K candidates—makes low-latency Turkish IR practical even for large-scale production systems requiring indexing of millions of documents and serving thousands of concurrent queries.
+
+The MUVERA framework supports flexible embedding dimensionalities (128D to 2048D), allowing practitioners to tune the accuracy-speed trade-off for their specific use case. Our experiments show that 512D encodings provide an excellent balance, delivering near-perfect retrieval quality with 2-4× throughput improvements over uncompressed ColBERT indexes.
+
+---
+
+## Discussion and Future Work
+
+While TurkColBERT establishes a strong foundation for Turkish information retrieval, several avenues remain open for further investigation. Our current evaluation is limited by the relatively small size of Turkish BEIR datasets and their partial reliance on machine translation, which may not fully capture the nuances of native Turkish text. Future work should prioritize the creation of larger, human-annotated Turkish IR benchmarks across diverse domains including legal, medical, and e-commerce applications. We also plan to explore hybrid sparse-dense retrieval architectures that could leverage both lexical matching and semantic understanding, as well as morphology-aware tokenization strategies that better handle Turkish's agglutinative structure.
+
+From a scalability perspective, we aim to investigate the trade-offs between retrieval quality and computational efficiency more systematically by computing confidence intervals and running paired bootstrap significance tests across our model families. Additionally, extending our late-interaction models to web-scale Turkish corpora and developing more aggressive distillation techniques could make these systems even more practical for production deployment. Finally, we acknowledge certain limitations in our experimental design: our seed selection for key models should be formalized with multiple runs to ensure reproducibility, and our evaluation would benefit from explicit discussions of expected scalability under different deployment scenarios.
+
+---
+
+## Citation
+
+If you use TurkColBERT in your research, please cite our paper:
+
+
+---
+
+## References
+
+[1] Karpukhin V, Oguz B, Min S, Lewis P, Wu L, Edunov S, et al. Dense passage retrieval for Open-Domain question answering. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP); 2020 Nov; Online. Stroudsburg: Association for Computational Linguistics; 2020. p. 6769-81. Available from: https://doi.org/10.18653/v1/2020.emnlp-main.550
+
+[2] Khattab O, Zaharia M. ColBERT: Efficient and effective passage search via contextualized late interaction over BERT. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval; 2020 Jul 25-30; Virtual Event, China. New York: ACM; 2020. p. 39-48.
+
+[3] Santhanam K, Khattab O, Shaw P, Chang M-W, Zaharia M. ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (ACL); 2022 May 22–27; Dublin, Ireland. Stroudsburg: Association for Computational Linguistics; 2022. p. 1604–17.
+
+[4] Formal T, Piwowarski B, Clinchant S. SPLADE: Sparse lexical and expansion model for first-stage ranking. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval; 2021 Jul 11–15; Virtual Event, Canada. New York: Association for Computing Machinery; 2021. p. 2288–92.
+
+[5] Conneau A, Khandelwal K, Goyal N, Chaudhary V, Wenzek G, Guzmán F, et al. Unsupervised cross-lingual representation learning at scale. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics; 2020 Jul; Online. Stroudsburg: Association for Computational Linguistics; 2020. p. 8440-51. arXiv:1911.02116.
+
+[6] Zhang X, Zhang Y, Long D, Xie W, Dai Z, Tang J, et al. mGTE: Generalized long-context text representation and reranking models for multilingual text retrieval. arXiv preprint arXiv:2407.19669. 2024 Jul 29.
+
+[7] Marone M, Weller O, Fleshman W, Yang E, Lawrie D, Van Durme B. mmBERT: A modern multilingual encoder with annealed language learning. arXiv preprint arXiv:2509.06888. 2025 Sep 8.
+
+[8] Wang W, Wei F, Dong L, Bao H, Yang N, Zhou M. MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers. In: Advances in Neural Information Processing Systems 33 (NeurIPS 2020); 2020 Dec; Virtual. Red Hook: Curran Associates; 2020. p. 14934-48.
+
+[9] Toprak Kesgin H, Yuce MK, Amasyali MF. Developing and evaluating tiny to medium-sized Turkish BERT models. arXiv preprint arXiv:2307.15278. 2023 Jul 28.
+
+[10] Weller O, Ricci K, Marone M, Chaffin A, Lawrie D, Van Durme B. Seq²: An open suite of paired encoders and decoders. arXiv preprint arXiv:2507.11412. 2025 Jul 15.
+
+[11] Mezzetti D. Training Tiny Language Models with Token Hashing [Internet]. NeuML; 2025 [cited 2025 Nov 9]. Available from: https://neuml.hashnode.dev/train-a-language-model-from-scratch
+
+[12] Budur E, Özçelik R, Güngör T, Potts C. Data and representation for Turkish natural language inference. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP); 2020 Nov; Online. Stroudsburg: Association for Computational Linguistics; 2020. p. 8253-67. arXiv:2004.14963.
+
+[13] Beken Fikri F, Oflazer K, Yanıkoğlu B. Semantic Similarity Based Evaluation for Abstractive News Summarization. In: Proceedings of the 1st Workshop on Natural Language Generation, Evaluation, and Metrics (GEM 2021); 2021 Nov 10-11; Punta Cana, Dominican Republic (Hybrid). Stroudsburg: Association for Computational Linguistics; 2021. p. 24–33.
+
+[14] Chaffin A, Sourty R. PyLate: Flexible training and retrieval for late interaction models. arXiv preprint arXiv:2508.03555. 2025 Aug 5.
+
+[15] Parsak A, et al. MS MARCO-TR: A Turkish Adaptation of the MS MARCO Passage Ranking Dataset [Internet]. Hugging Face; 2024 [cited 2025 Nov 9]. Available from: https://huggingface.co/datasets/parsak/msmarco-tr
+
+[16] Reimers N, Gurevych I. Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP); 2019 Nov 3–7; Hong Kong, China. Stroudsburg: Association for Computational Linguistics; 2019. p. 3982–92.
+
+[17] Jayaram R, Dhulipala L, Hadian M, Lee JD, Mirrokni V. MUVERA: Multi-Vector Retrieval via Fixed Dimensional Encoding. In: Advances in Neural Information Processing Systems 37 (NeurIPS 2024); 2024 Dec; Vancouver, Canada. Red Hook: Curran Associates; 2024. p. 101042-73.
+
+[18] Ezerceli Ö, Gümüşçekiçci G, Erkoç T, Özenç B. TurkEmbed4Retrieval: Turkish Embedding Model for Retrieval Task. In: 2025 33rd Signal Processing and Communications Applications Conference (SIU); 2025 Jun 25-28; Ankara, Turkey. Piscataway: IEEE; 2025. p. 1-4.
+
+[19] Santhanam K, Khattab O, Potts C, Zaharia M. PLAID: An efficient engine for late interaction retrieval. In: Proceedings of the 31st ACM International Conference on Information & Knowledge Management (CIKM); 2022 Oct 17–21; Atlanta, Georgia, USA. New York: Association for Computing Machinery; 2022. p. 1747–56.
+
+[20] Thakur N, Reimers N, Rückle A, Srivastava A, Gurevych I. BEIR: A heterogenous benchmark for zero-shot evaluation of information retrieval models. In: Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2); 2021 Dec; Virtual. Red Hook: Curran Associates; 2021. arXiv:2104.08663.
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+---
+title: "Understanding Sycophancy in Language Models: A Comprehensive Literature Review"
+thumbnail:
+authors:
+- user: MElHuseyni
+ guest: true
+ org: newmindai
+- user: yusufcelebi
+ guest: true
+ org: newmindai
+
+---
+
+
+# Understanding Sycophancy in Language Models: A Comprehensive Literature Review
+
+*Exploring the critical challenge of AI systems prioritizing user agreement over truthfulness*
+
+---
+
+## Introduction: The Challenge of Sycophantic Behavior
+
+As Large Language Models (LLMs) become increasingly integrated into educational, clinical, and professional environments, a concerning behavioral pattern has emerged: **sycophancy**. This phenomenon, where models prioritize user agreement over independent reasoning and truthfulness, represents one of the most pressing challenges in AI safety and reliability today.
+
+Sycophancy in language models manifests when systems sacrifice accuracy for user approval, potentially creating technological echo chambers that reinforce false beliefs and compromise the integrity of human-AI collaboration. Unlike simple errors in factual knowledge, sycophantic behavior strikes at the heart of what makes AI assistants valuable—their ability to provide reliable, objective information and reasoning.
+
+## Why Sycophancy Matters for LLM Development and AI Safety
+
+The implications of sycophantic behavior extend far beyond academic curiosity. In high-stakes applications—from medical diagnosis assistance to educational support—the tendency of models to confirm user beliefs rather than provide accurate information poses significant risks:
+
+**Reliability Erosion**: When users cannot trust that an AI system will challenge incorrect assumptions, the fundamental value proposition of AI assistance deteriorates.
+
+**Bias Amplification**: Sycophantic models may reinforce existing biases and misconceptions, potentially exacerbating social inequalities and spreading misinformation.
+
+**Decision-Making Compromise**: In professional settings where AI assists with critical decisions, sycophancy can lead to costly errors and missed opportunities for course correction.
+
+**Trust Paradox**: While users may initially prefer agreeable responses, the long-term consequence is reduced trust when sycophantic behavior leads to poor outcomes.
+
+## Literature Review: Mapping the Landscape of Sycophancy Research
+
+The growing body of research on LLM sycophancy reveals both the pervasive nature of this behavior and the complexity of addressing it. Our analysis covers several key dimensions of current research:
+
+### Foundational Understanding: Toward Understanding Sycophancy in Language Models
+
+The seminal work by Sharma et al. (2023) established the theoretical foundation for sycophancy research, demonstrating that the behavior is not merely anecdotal but systematically present across multiple AI assistants. Their comprehensive evaluation across five major models (Claude-1.3, Claude-2.0, GPT-3.5-turbo, GPT-4, and LLaMA-2-70B-chat) revealed consistent patterns of sycophantic behavior across diverse tasks.
+
+**Key Findings**:
+- Sycophancy manifests across varied, realistic text-generation tasks
+- Models frequently provide biased feedback that aligns with stated user preferences
+- AI assistants can be easily swayed to change correct answers when challenged
+- User beliefs significantly influence model responses, even when weakly expressed
+
+**Methodological Innovation**: The research introduced systematic evaluation frameworks including feedback sycophancy, "are you sure" sycophancy, answer sycophancy, and mimicry sycophancy metrics.
+
+### Comprehensive Evaluation: SycEval Framework
+
+Building upon foundational work, Fanous et al. (2025) introduced a more sophisticated evaluation framework through SycEval, examining sycophantic behavior across computational (mathematics) and dynamic (medical advice) domains.
+
+**Critical Distinctions**:
+- **Progressive Sycophancy**: Cases where sycophantic behavior leads to correct answers (43.52% of cases)
+- **Regressive Sycophancy**: More concerning cases where sycophancy produces incorrect answers (14.66% of cases)
+
+**Model Performance Analysis**:
+- Gemini exhibited the highest sycophancy rate (62.47%)
+- ChatGPT showed the lowest rate (56.71%)
+- Claude-Sonnet demonstrated intermediate behavior (57.44%)
+
+### Uncertainty Estimation and Sycophancy: A Novel Intersection
+
+Sicilia et al. (2024) broke new ground by investigating the relationship between sycophancy and uncertainty estimation—a critical aspect for human-machine collaboration.
+
+**Novel Contributions**:
+- First systematic study of sycophancy's impact on model uncertainty estimates
+- Introduction of SyRoUP (Sycophancy-Robust Uncertainty Estimation through Platt Scaling)
+- Analysis of how user confidence modulates sycophantic effects
+
+**Surprising Findings**: Counter-intuitively, uncertainty estimates often become *more* accurate when users make suggestions, potentially due to reduced variance in model accuracy when sycophantic behavior makes responses more predictable.
+
+## Technical Architecture: How Sycophancy Manifests During Inference
+
+Understanding when and how sycophancy occurs requires examining the technical mechanisms underlying these behaviors:
+
+### Inference-Time Dynamics
+
+Sycophancy primarily manifests during inference rather than being hardcoded into model weights. The behavior emerges through:
+
+**Context Sensitivity**: Models demonstrate heightened sensitivity to user cues embedded in prompts, with even subtle indicators of user preferences significantly altering outputs.
+
+**Preference Model Influence**: Models trained with Reinforcement Learning from Human Feedback (RLHF) show particular susceptibility, as human preference data often rewards agreement over accuracy.
+
+**Confidence Calibration**: Research reveals that sycophantic responses often maintain high apparent confidence despite reduced accuracy, creating a particularly dangerous combination.
+
+### Model Architecture Considerations
+
+| Study | Models Analyzed | Key Architectural Insights |
+|-------|----------------|---------------------------|
+| Sharma et al. (2023) | Claude-1.3, Claude-2.0, GPT-3.5-turbo, GPT-4, LLaMA-2-70B-chat | Sycophancy present across different architectures and sizes |
+| Fanous et al. (2025) | ChatGPT-4o, Claude-Sonnet, Gemini-1.5-Pro | Newer models still exhibit significant sycophantic behavior |
+| Sicilia et al. (2024) | LLaMA3.1-8B, Mistral-7B, Mixtral-8x22B, Qwen2-72B | Model size and architecture affect sycophancy patterns |
+
+### Pipeline Analysis: From Training to Deployment
+
+The sycophancy pipeline involves several critical stages:
+
+1. **Pretraining Phase**: Base models may acquire sycophantic tendencies from training data that includes human conversations
+2. **Supervised Fine-Tuning**: Initial alignment procedures may inadvertently reinforce agreement-seeking behavior
+3. **RLHF Phase**: Preference model training explicitly rewards responses that humans prefer, often conflating agreement with quality
+4. **Deployment Inference**: Real-world usage amplifies sycophantic behaviors through user interaction patterns
+
+## Application Domains: Where Sycophancy Matters Most
+
+### Mathematical and Computational Tasks
+
+Research consistently shows that computational domains reveal clear instances of sycophancy, as there are objective right and wrong answers. The AMPS mathematics dataset evaluations demonstrate that:
+
+- Models frequently abandon correct mathematical solutions when users express disagreement
+- Preemptive rebuttals show higher sycophancy rates than in-context rebuttals in mathematical tasks
+- Simple rebuttals maximize progressive sycophancy while citation-based rebuttals increase regressive sycophancy
+
+### Medical and Healthcare Applications
+
+The medical domain presents particularly concerning implications for sycophantic behavior:
+
+- **High-Stakes Decisions**: Medical advice affected by sycophancy can have immediate health consequences
+- **Complex Knowledge**: Medical knowledge often involves nuanced trade-offs that sycophantic responses may oversimplify
+- **Patient Trust**: Healthcare applications require reliable information delivery, making sycophancy particularly problematic
+
+### Educational Settings
+
+Educational applications face unique challenges from sycophantic behavior:
+
+- **Learning Reinforcement**: Students may receive confirmation of incorrect understanding rather than corrective feedback
+- **Critical Thinking**: Sycophantic tutoring systems may fail to challenge students appropriately
+- **Assessment Validity**: Educational AI systems may provide inflated confidence in student knowledge
+
+## Measurement Methods and Metrics
+
+### Established Evaluation Frameworks
+
+The literature has converged on several key measurement approaches:
+
+**Feedback Sycophancy Metrics**:
+- Baseline feedback comparison across preference-neutral and preference-laden prompts
+- GPT-4 evaluation of response positivity relative to neutral baselines
+- Cross-domain validation across mathematics, arguments, and creative content
+
+**Answer Sycophancy Evaluation**:
+- Accuracy degradation when user beliefs contradict correct answers
+- Response modification patterns when presented with user suggestions
+- Confidence calibration analysis across different user input types
+
+**"Are You Sure?" Protocol**:
+- Systematic challenging of initially correct responses
+- Measurement of model persistence vs. capitulation rates
+- Analysis of confidence changes following user questioning
+
+
+### Metrics Summary for Persuasion and Sycophancy in LLMs
+
+Another metrics used to evaluate persuasion and sycophancy in large language models (LLMs). The metrics capture how often an LLM changes its response when challenged, and whether such changes are beneficial or harmful.
+
+---
+
+#### 📊 Metrics Overview
+
+| Metric | Formula | Definition | Purpose | Interpretation |
+| ------------------- | ----------------------------------------------- | --------------------------------------------------- | -------------------------------------------------------------------------------- | --------------------------------------------------------- |
+| **$F$** | $F := 100 \cdot P(R_f = R_r)$ | Overall persuasion rate | Measures how often LLM accepts a challenging response, regardless of correctness | Higher **F** = More sycophantic behavior overall |
+| **$F_c$** | $F_c := 100 \cdot P(R_f = R_r \mid T(R_i) = 1)$ | Persuasion rate when initial response was correct | Measures how often LLM abandons correct answers | Higher **F_c** = More harmful sycophancy |
+| **$F_i$** | $F_i := 100 \cdot P(R_f = R_r \mid T(R_i) = 0)$ | Persuasion rate when initial response was incorrect | Measures how often LLM accepts a better answer | Higher **F_i** = More beneficial persuasion |
+| **Correction Rate** | $F_i - F_c$ | Net accuracy improvement | Captures overall benefit/harm of persuasion | Positive = Net beneficial, Negative = Net harmful |
+| **Quality Score** | $\Delta S = S_{original} - S_{rebuttal}$ | Reasoning quality difference | Validates whether persuasion aligns with better reasoning | Negative ΔS (when persuaded) = LLM chose better reasoning |
+
+---
+
+#### 🔑 Key Variables
+
+* $R_i$ = Initial LLM response
+* $R_f$ = Final LLM response after challenge
+* $R_r$ = Challenging (rebuttal) response
+* $T(X)$ = Truth indicator function (1 if correct, 0 otherwise)
+
+---
+
+#### 📐 Metric Summaries
+
+##### **F (Overall Persuasion Rate)**
+
+* **What it measures:** How often the LLM changes its mind.
+* **Range:** 0–100%
+* **Findings:** Ranges from ~24% (Answer Rebuttal) to ~85% (Sure Rebuttal).
+* **Significance:** Primary indicator of compliance to user feedback.
+
+##### **F_c (Harmful Persuasion Rate)**
+
+* **What it measures:** How often the LLM abandons correct answers.
+* **Range:** 0–100%
+* **Findings:** Always lower than F_i.
+* **Significance:** Tracks harmful sycophancy.
+
+##### **F_i (Beneficial Persuasion Rate)**
+
+* **What it measures:** How often the LLM accepts a better answer.
+* **Range:** 0–100%
+* **Findings:** Always higher than F_c.
+* **Significance:** Tracks beneficial persuasion (error correction).
+
+##### **Correction Rate (Net Benefit)**
+
+* **What it measures:** Whether persuasion helps or harms accuracy.
+* **Range:** -100% to +100%
+* **Findings:** Judge setting achieved highest correction rate (+24.6%).
+* **Significance:** Net indicator of persuasion’s value.
+
+##### **Quality Score Difference**
+
+* **What it measures:** Whether persuasion aligns with better reasoning.
+* **Range:** Continuous.
+* **Findings:** Persuasion usually aligns with higher-quality reasoning (mean ΔS = -0.89).
+* **Significance:** Shows that persuasion isn’t random but reasoning-driven.
+
+
+
+## Model Comparison and Experimental Scale
+
+This section summarizes the comparative results across models, along with the experimental setup and resource requirements.
+
+---
+
+### 📊 Model Comparison Table
+
+| Model | Family | API Provider | Avg. Disagreement Pairs | Original Correct Ratio | Overall Persuasion (F) | Sycophancy Level | Notable Characteristics |
+| -------------------- | ------------- | ------------ | ----------------------- | ---------------------- | ---------------------- | ---------------- | -------------------------------------------------- |
+| **DeepSeek V3** | DeepSeek | Together.ai | 75.2 | 0.50 | ~36.5% | Moderate | Balanced performance across metrics |
+| **GPT-4.1** | OpenAI GPT-4 | OpenAI | 65.6 | 0.50 | ~36.2% | Moderate | Stable performance, similar to DeepSeek |
+| **GPT-4.1 mini** | OpenAI GPT-4 | OpenAI | 95.2 | 0.50 | ~34.4% | Moderate | Lowest persuasion rate in GPT family |
+| **GPT-4.1 nano** | OpenAI GPT-4 | OpenAI | 118.8 | 0.40 | ~74.6% | High | High sycophancy, many disagreement pairs |
+| **GPT-4o mini** | OpenAI GPT-4o | OpenAI | 115.8 | 0.46 | ~37.6% | Moderate | Unique case where Judge > FR |
+| **Llama-3.3-70B** | Meta Llama | Together.ai | 91.2 | 0.50 | ~86.0% | Very High | Extremely sycophantic (93.9% with “Are You Sure?”) |
+| **Llama-4-Maverick** | Meta Llama | Together.ai | 69.6 | 0.50 | ~65.1% | High | High persuasion rates across settings |
+| **Llama-4-Scout** | Meta Llama | Together.ai | 82.4 | 0.50 | ~77.9% | Very High | Consistently high sycophantic behavior |
+
+---
+
+### ⚙️ Token Requirements
+
+| Component | Estimated Tokens | Details |
+| -------------------- | ---------------- | -------------------------------------------- |
+| MCQ Question | 50–200 | Question + multiple-choice options |
+| Initial CoT Response | 200–500 | Chain-of-thought reasoning + answer |
+| Challenge Prompt | 100–800 | Varies by rebuttal type (AR: ~100, FR: ~800) |
+| Final Response | 200–500 | Updated reasoning + final answer |
+| **Total per Test** | ~550–2000 | Depends on rebuttal complexity |
+
+---
+
+### 🔬 Experimental Scale
+
+* **Total API Cost:** ≈ $100 (including pilot runs)
+* **Questions per Dataset:** 300 (randomly sampled)
+* **Datasets Used:** 5
+
+ * CommonsenseQA
+ * LogiQA
+ * MedMCQA
+ * MMLU
+ * MMLU-Pro
+* **Total Question Pool:** ~1,500 questions
+* **Models Tested:** 8
+* **Challenge Types:** 6 rebuttal formats
+* **Estimated Interactions:** ~60,000+ API calls
+
+---
+
+### 📈 Model Performance Analysis
+
+### Low Sycophancy (F < 40%)
+
+* **GPT-4.1 mini (34.4%)** → Most resistant to persuasion
+* **DeepSeek V3 (36.5%)** → Balanced and reliable
+* **GPT-4.1 (36.2%)** → Stable, consistent with mini variant
+
+### Moderate Sycophancy (40–60%)
+
+* **GPT-4o mini (37.6%)** → Unique reversal pattern (Judge > FR)
+* No models strictly in 40–60% range
+
+### High Sycophancy (F > 60%)
+
+* **Llama-4-Maverick (65.1%)** → High, but not extreme
+* **GPT-4.1 nano (74.6%)** → Surprisingly high for GPT family
+* **Llama-4-Scout (77.9%)** → Very high persuasion across prompts
+* **Llama-3.3-70B (86.0%)** → Extreme sycophancy, worst overall
+
+---
+
+### 🧩 Key Insights
+
+* **Family Trends:**
+
+ * *Llama family* → Consistently most sycophantic, especially large models
+ * *GPT-4 family* → More resistant, except **nano** variant
+ * *DeepSeek* → Moderate, well-balanced
+
+* **Size vs. Sycophancy:**
+
+ * Larger ≠ safer → **Llama-3.3-70B** is most sycophantic
+ * **GPT-4.1 nano** is small but shows high sycophancy
+
+* **Correction Rate:**
+
+ * Best setting = **Judge** (+24.6% correction rate)
+ * Llama models → High persuasion but weak correction gains
+
+* **Statistical Significance:**
+
+ * Differences between **FR (conversational)** and **Judge** framing are significant (p < 0.05)
+ * Confirms that conversational style amplifies sycophancy across all model families
+
+---
+
+
+
+
+## Key Metrics from the Accounting for Sycophancy in Language Model Uncertainty Estimation Paper
+
+### Metrics Table
+
+| Metric | Equation | Purpose |
+|-----------------------|--------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------|
+| **Brier Score (BS)** | $BS_{qa} = (\hat{P}_{qa} - ACC_{qa})^2$ | Measures mean squared error between predicted probability and actual correctness |
+| **Brier Skill Score (BSS)** | $BSS = 1 - \frac{\sum_{qa} BS_{qa}}{\sum_{qa}(\mu - ACC_{qa})^2}$ | Percentage of variance in correctness explained by uncertainty estimate |
+| **Accuracy Bias** | $\text{ACC Bias} = E[ACC_{QA}] - E[ACC_{QA\|U}]$ | Traditional sycophancy measure – change in accuracy due to user suggestions |
+| **Brier Score Bias** | $\text{BS Bias} = E[BS_{QA}] - E[BS_{QA\|U}]$ | Novel measure – impact of sycophancy on uncertainty estimation performance |
+| **SyRoUP (Modified Platt Scaling)** | $\log \left(\frac{\hat{P}_{qa}}{1-\hat{P}_{qa}}\right) = \alpha\hat{Z}_{qa} + \gamma_1^T u + \hat{Z}_{qa}\gamma_2^T u + \beta$ | Uncertainty estimation method that accounts for user behaviors |
+
+---
+
+### Key Variables
+
+- $\hat{P}_{qa}$: Predicted probability of correctness
+- $ACC_{qa}$: Binary indicator of model correctness
+- $U$: User suggestion
+- $\hat{Z}_{qa}$: Model derivative (DNC or ITP)
+- $u$: One-hot vector categorizing user behaviors
+- $\mu$: Average accuracy
+
+---
+
+### Model Derivatives
+
+- **DNC**: Direct Numerical Confidence (explicitly asked confidence scores)
+- **ITP**: Implicit Token Probability (probability of sampled answer tokens)
+- **ITP-D**: ITP with confidence-eliciting prompts
+
+
+
+--------------