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Liang Pang

6 papers in the PaperMetrix corpus

Publications

Papers by this author

  1. LoL: A Comparative Regularization Loss over Query Reformulation Losses for Pseudo-Relevance Feedback

    2022 · arXiv (Cornell University)

    Pseudo-relevance feedback (PRF) has proven to be an effective query reformulation technique to improve retrieval accuracy. It aims to alleviate the mismatch of linguistic expressions between a query and its potential relevant documents. Existing PRF …

  2. Can Small Language Models be Good Reasoners for Sequential Recommendation?

    2024 · arXiv (Cornell University)

    Large language models (LLMs) open up new horizons for sequential recommendations, owing to their remarkable language comprehension and generation capabilities. However, there are still numerous challenges that should be addressed to successfully implement sequential recommendations …

  3. Beyond Memorization: The Challenge of Random Memory Access in Language Models

    2024

    Recent developments in Language Models (LMs) have shown their effectiveness in NLP tasks, particularly in knowledge-intensive tasks.However, the mechanisms underlying knowledge storage and memory access within their parameters remain elusive.In this paper, we investigate whether …

  4. RLKD: Distilling LLMs' Reasoning via Reinforcement Learning

    2025 · arXiv (Cornell University)

    Distilling reasoning paths from teacher to student models via supervised fine-tuning (SFT) provides a shortcut for improving the reasoning ability of smaller Large Language Models (LLMs). However, the reasoning paths generated by teacher models often …

  5. A Deep Architecture for Semantic Matching with Multiple Positional Sentence Representations

    2016 · Proceedings of the AAAI Conference on Artificial Intelligence

    Matching natural language sentences is central for many applications such as information retrieval and question answering. Existing deep models rely on a single sentence representation or multiple granularity representations for matching. However, such methods cannot …

  6. A Deep Architecture for Semantic Matching with Multiple Positional Sentence Representations

    2015 · arXiv (Cornell University)

    Matching natural language sentences is central for many applications such as information retrieval and question answering. Existing deep models rely on a single sentence representation or multiple granularity representations for matching. However, such methods cannot …