Papers with Augmenting Large Language Models
Entropy-Based Decoding for Retrieval-Augmented Large Language Models (2025.naacl-long)
Copied to clipboard
| Challenge: | Despite their success, retrieval-augmented LLMs still face the distractibility issue, where the generated responses are negatively influenced by noise from both external and intrinsic knowledge sources. |
| Approach: | They propose a entropy-based document-parallel ensemble decoding method that prioritizes low-entropies from retrieved documents and incorporates a contrastive decoding mechanism that contrasts the obtained low- and high-entropic ensemble distributions with the high-end internal knowledge across layers. |
| Outcome: | The proposed method improves on open-domain question answering datasets and shows that it is highly efficient. |
Learning When to Retrieve, What to Rewrite, and How to Respond in Conversational QA (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Understanding users’ contextual search intent when generating responses is an understudied topic for conversational question answering (QA). |
| Approach: | They propose a method that allows LLMs to decide when to retrieve in RAG settings given a conversational context. |
| Outcome: | The proposed method improves on three conversational QA datasets and criticizes the quality of generated responses. |
LoopTool: Closing the Data–Training Loop for Robust LLM Tool Calls (2026.acl-long)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) are powerful tools for multi-step tasks, but static data pipelines hinder tool learning and cause noisy labels to persist. |
| Approach: | They propose a fully automated, model-aware data evolution framework that tightly integrates data synthesis and model training. |
| Outcome: | Experiments show that LoopTool-8B significantly surpasses its 32B data generator and achieves new state-of-the-art results on the BFCL-v3 and ACEBench benchmarks for its scale. |