Papers by Michael Bendersky
Take One Step at a Time to Know Incremental Utility of Demonstration: An Analysis on Reranking for Few-Shot In-Context Learning (2024.naacl-long)
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| Challenge: | Recent advances of Large Language Models (LLMs) have been pushing the field of Natural Language Processing (NLP) to the next level in many different aspects. |
| Approach: | They propose a novel labeling method which estimates how much incremental knowledge is brought into LLMs by a demonstration. |
| Outcome: | The proposed method estimates how much incremental knowledge is brought into the LLMs by a demonstration. |
QUILL: Query Intent with Large Language Models using Retrieval Augmentation and Multi-stage Distillation (2022.emnlp-industry)
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| Challenge: | Large Language Models (LLMs) have shown impressive results on a variety of text understanding tasks. |
| Approach: | They propose a two-stage distillation approach that allows retrieval augmentation to be carried over without the increased compute associated with it. |
| Outcome: | The proposed approach can carry over the gains of retrieval augmentation without suffering the increased compute typically associated with it. |
DiPair: Fast and Accurate Distillation for Trillion-Scale Text Matching and Pair Modeling (2020.findings-emnlp)
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Jiecao Chen, Liu Yang, Karthik Raman, Michael Bendersky, Jung-Jung Yeh, Yun Zhou, Marc Najork, Danyang Cai, Ehsan Emadzadeh
| Challenge: | Existing knowledge distillation models are not optimized for dealing with pairs (or tuples) of texts. |
| Approach: | They propose a framework for distilling fast and accurate models on text pair tasks using a scalable end-to-end training strategy. |
| Outcome: | Empirical studies on academic and real-world e-commerce benchmarks show the proposed framework can achieve speedups of over 350x and minimal quality drop relative to the cross-attention teacher BERT model. |
Predicting Text Preference Via Structured Comparative Reasoning (2024.acl-long)
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Jing Nathan Yan, Tianqi Liu, Justin Chiu, Jiaming Shen, Zhen Qin, Yue Yu, Charumathi Lakshmanan, Yair Kurzion, Alexander Rush, Jialu Liu, Michael Bendersky
| Challenge: | Existing approaches to comparative reasoning rely on pretraining or fine-tuning models at the cost of massive human annotation and computation. |
| Approach: | They propose a model that prompts LLMs to generate structured intermediate comparisons by proposing aspects for comparison, followed by generating textual comparisons under each aspect. |
| Outcome: | The proposed model significantly reduces hallucination and improves consistency across various NLP tasks. |
Beyond Yes and No: Improving Zero-Shot LLM Rankers via Scoring Fine-Grained Relevance Labels (2024.naacl-short)
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| Challenge: | Existing pointwise LLMs provide noisy or biased answers for documents that are partially relevant to the query. |
| Approach: | They propose to incorporate fine-grained relevance labels into the LLM prompt . they propose to better differentiate between documents with different levels of relevance . |
| Outcome: | The proposed model can differentiate between documents with different levels of relevance to the query and derive a more accurate ranking. |
Multilingual Fine-Grained News Headline Hallucination Detection (2024.findings-emnlp)
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| Challenge: | Existing models to generate news headlines often suffer from the "hallucination" problem, where the produced headline does not fully align with the source article's content. |
| Approach: | They propose to use a multilingual, fine-grained dataset to detect news headlines in 5 languages using supervised fine-tuning techniques and coarse-to-fine prompting to boost the few-shot detection performance. |
| Outcome: | The proposed methods boost the few-shot hallucination detection performance in terms of the example-F1 metric. |
It’s All Relative! – A Synthetic Query Generation Approach for Improving Zero-Shot Relevance Prediction (2024.findings-naacl)
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| Challenge: | Large language models generate synthetic query-document pairs by prompting with as few as 8 demonstrations. |
| Approach: | They propose to generate queries simultaneously for different labels by prompting with 8 demonstrations. |
| Outcome: | Extensive experimentation shows that synthetic queries generated in such a fashion improve performance. |
Consolidating Ranking and Relevance Predictions of Large Language Models through Post-Processing (2024.emnlp-main)
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| Challenge: | Existing approaches to generate relevance labels for large language models have not been successful in generating relevance labels. |
| Approach: | They propose a method to combine LLM relevance labels with ranking abilities . they take both LLM generated relevance labels and pairwise preferences . |
| Outcome: | The proposed method balances the ranking and labeling abilities of large language models . it takes both LLM generated relevance labels and pairwise preferences . |
LaMP: When Large Language Models Meet Personalization (2024.acl-long)
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| Challenge: | Existing benchmarks for personalization in large language models are understudied . |
| Approach: | They propose a benchmark for training and evaluating language models for producing personalized outputs using a set of seven personalized tasks . they propose two retrieval augmentation approaches that retrieve personal items from each user profile for personalizing language model outputs. |
| Outcome: | The proposed approach is effective for a set of zero-shot and fine-tuned language models and highlights the impact of personalization in various natural language tasks. |
PRewrite: Prompt Rewriting with Reinforcement Learning (2024.acl-short)
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| Challenge: | Prompt engineering is done manually in a trial-and-error ad-hoc fashion, authors say . |
| Approach: | They propose a method to rewrite an under-optimized prompt to a more effective prompt. |
| Outcome: | The proposed method rewrites an under-optimized prompt to a more effective prompt. |
PLaD: Preference-based Large Language Model Distillation with Pseudo-Preference Pairs (2024.findings-acl)
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Rongzhi Zhang, Jiaming Shen, Tianqi Liu, Haorui Wang, Zhen Qin, Feng Han, Jialu Liu, Simon Baumgartner, Michael Bendersky, Chao Zhang
| Challenge: | Knowledge distillation (KD) is a technique for transferring expertise from large teacher models to compact student models with reduced memory footprints and inference costs. |
| Approach: | They propose to transfer knowledge from large teacher models to compact student models by exploiting teacher-student capacity discrepancies to generate pseudo-preference pairs where teacher outputs are preferred over student outputs. |
| Outcome: | The proposed framework exploits teacher-student capacity discrepancy to generate pseudo-preference pairs where teacher outputs are preferred over student outputs. |
Creator Context for Tweet Recommendation (2023.emnlp-industry)
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Spurthi Amba Hombaiah, Tao Chen, Mingyang Zhang, Michael Bendersky, Marc Najork, Matt Colen, Sergey Levi, Vladimir Ofitserov, Tanvir Amin
| Challenge: | In tweets, people refer to the content it delivers, but also to the person behind it. |
| Approach: | They examine how creator context can be used to advance tweet understanding by recommending relevant tweets to news articles. |
| Outcome: | The proposed model can improve a news article's relevance by recommending relevant tweets to news articles. |
Large Language Models are Effective Text Rankers with Pairwise Ranking Prompting (2024.findings-naacl)
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Zhen Qin, Rolf Jagerman, Kai Hui, Honglei Zhuang, Junru Wu, Le Yan, Jiaming Shen, Tianqi Liu, Jialu Liu, Donald Metzler, Xuanhui Wang, Michael Bendersky
| Challenge: | Existing methods to rank documents using large language models do not understand these challenging ranking formulations. |
| Approach: | They propose to use Pairwise Ranking Prompting to improve ranking performance . they propose to outperform fine-tuned baseline rankers on benchmark datasets . |
| Outcome: | The proposed technique outperforms supervised baselines on benchmark datasets and outperformed other LLM-based solutions by over 10% on average. |
Retrieval Augmented Generation or Long-Context LLMs? A Comprehensive Study and Hybrid Approach (2024.emnlp-industry)
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| Challenge: | Recent LLMs like Gemini-1.5 and GPT-4 show exceptional capabilities to understand long contexts directly. |
| Approach: | They propose a method that routes queries to RAG or LC based on model self-reflection. |
| Outcome: | The proposed method significantly reduces the computation cost while maintaining a comparable performance to RAG. |
Explanation-aware Soft Ensemble Empowers Large Language Model In-context Learning (2024.acl-long)
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Yue Yu, Jiaming Shen, Tianqi Liu, Zhen Qin, Jing Nathan Yan, Jialu Liu, Chao Zhang, Michael Bendersky
| Challenge: | Recent advances in natural language processing (NLP) have witnessed the remarkable capabilities of Large Language Models (LLMs). |
| Approach: | They propose an Explanation-Aware Soft Ensemble framework to empower in-context learning with Large language models. |
| Outcome: | The proposed framework can be used to enhance in-context learning on seven natural language understanding tasks and four varying-size LLMs. |
Bridging the Preference Gap between Retrievers and LLMs (2024.acl-long)
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| Challenge: | Existing studies on retrievers and LLMs treat them as separate components . a novel bridge model is proposed to optimize the relationship between the retriever and the LLM . |
| Approach: | They propose a framework that chains together supervised and reinforcement learning to train a bridge model that optimizes the connection between the retriever and the LLM. |
| Outcome: | Empirical results show that the proposed model optimizes the connection between the retriever and the LLM. |