1 citations · 1 across the 8 of their papers we have counts for
8 papers
SAND: Boosting LLM Agents with Self-Taught Action Deliberation
Yu Xia, Yiran Shen, Junda Wu +5
Large Language Model (LLM) agents are commonly tuned with supervised finetuning on ReAct-style expert trajectories or preference optimization over pairwise rollouts. Most of these…
Towards Improving Long-Tail Entity Predictions in Temporal Knowledge Graphs through Global Similarity and Weighted Sampling
Mehrnoosh Mirtaheri, Ryan A. Rossi, Sungchul Kim +4
Temporal Knowledge Graph (TKG) completion models traditionally assume access to the entire graph during training. This overlooks challenges stemming from the evolving nature of TKG…
Disambiguation in Conversational Question Answering in the Era of LLMs and Agents: A Survey
Md Mehrab Tanjim, Yeonjun In, Xiang Chen +8
Ambiguity remains a fundamental challenge in Natural Language Processing (NLP) due to the inherent complexity and flexibility of human language. With the advent of Large Language M…
Pandora with Inaccurate Priors
Kiarash Banihashem, Xiang Chen, MohammadTaghi Hajiaghayi +4
We investigate the role of inaccurate priors for the classical Pandora's box problem. In the classical Pandora's box problem we are given a set of boxes each with a known cost and…
Numerical Pruning for Efficient Autoregressive Models
Xuan Shen, Zhao Song, Yufa Zhou +12
Transformers have emerged as the leading architecture in deep learning, proving to be versatile and highly effective across diverse domains beyond language and image processing. Ho…
CodeLutra: Boosting LLM Code Generation via Preference-Guided Refinement
Leitian Tao, Xiang Chen, Tong Yu +4
Large Language Models (LLMs) have revolutionized code generation but require significant resources and often over-generalize, limiting their task-specific efficiency. Fine-tuning s…
Diversify-verify-adapt: Efficient and Robust Retrieval-Augmented Ambiguous Question Answering
Yeonjun In, Sungchul Kim, Ryan A. Rossi +4
The retrieval augmented generation (RAG) framework addresses an ambiguity in user queries in QA systems by retrieving passages that cover all plausible interpretations and generati…
X-Reflect: Cross-Reflection Prompting for Multimodal Recommendation
Hanjia Lyu, Ryan Rossi, Xiang Chen +4
Large Language Models (LLMs) have been shown to enhance the effectiveness of enriching item descriptions, thereby improving the accuracy of recommendation systems. However, most ex…