4 citations · 6 across the 10 of their papers we have counts for
7 papers · 1 filter
CAST: Critique-Aware Supervision for Training Reliable Long-Horizon Tool-Calling Agents
Amir Saeidi, Zehua Zhang, Rishitosh Singh +6
Large language model (LLM) agents are increasingly deployed in long-horizon, interactive, and stateful environments. In these settings, a single wrong action, such as refunding the…
How Can Input Reformulation Improve Tool Usage Accuracy in a Complex Dynamic Environment? A Study on -bench
Venkatesh Mishra, Amir Saeidi, Satyam Raj +5
Recent advances in reasoning and planning capabilities of large language models (LLMs) have enabled their potential as autonomous agents capable of tool use in dynamic environments…
UnSeenTimeQA: Time-Sensitive Question-Answering Beyond LLMs' Memorization
Md Nayem Uddin, Amir Saeidi, Divij Handa +5
This paper introduces UnSeenTimeQA, a novel data contamination-free time-sensitive question-answering (TSQA) benchmark. It differs from existing TSQA benchmarks by avoiding web-sea…
Investigating and Addressing Hallucinations of LLMs in Tasks Involving Negation
Neeraj Varshney, Satyam Raj, Venkatesh Mishra +4
Large Language Models (LLMs) have achieved remarkable performance across a wide variety of natural language tasks. However, they have been shown to suffer from a critical limitatio…
Triple Preference Optimization: Achieving Better Alignment using a Single Step Optimization
Amir Saeidi, Shivanshu Verma, Aswin RRV +2
Reinforcement Learning with Human Feedback (RLHF) enhances the alignment of Large Language Models (LLMs). However, its limitations have led to the development of Direct Preference…
Insights into Alignment: Evaluating DPO and its Variants Across Multiple Tasks
Amir Saeidi, Shivanshu Verma, Md Nayem Uddin +1
This study evaluates Direct Preference Optimization (DPO) and its variants for aligning Large Language Models (LLMs) with human preferences, testing three configurations: (1) with…
When "Competency" in Reasoning Opens the Door to Vulnerability: Jailbreaking LLMs via Novel Complex Ciphers
Divij Handa, Zehua Zhang, Amir Saeidi +4
Recent advancements in Large Language Model (LLM) safety have primarily focused on mitigating attacks crafted in natural language or common ciphers (e.g. Base64), which are likely…