6 papers
Improving Language Agents through BREW: Bootstrapping expeRientially-learned Environmental knoWledge
Shashank Kirtania, Param Biyani, Priyanshu Gupta +4
Large Language Model (LLM)-based agents are increasingly capable of complex, multi-step tasks such as GUI automation, tool use, and data manipulation, yet they cannot learn from ex…
IndiMathBench: Autoformalizing Mathematical Reasoning Problems with a Human Touch
Param Biyani, Shashank Kirtania, Yasharth Bajpai +2
Reliable autoformalization remains challenging even in the era of large language models (LLMs). The scarcity of high-quality training data is a major bottleneck. Expert annotation…
STACKFEED: Structured Textual Actor-Critic Knowledge Base Editing with FeedBack
Shashank Kirtania, Naman Gupta, Priyanshu Gupta +7
Large Language Models (LLMs) often generate incorrect or outdated information, especially in low-resource settings or when dealing with private data. To address this, Retrieval-Aug…
Steering LLMs for Formal Theorem Proving
Shashank Kirtania, Arun Iyer
Recent advances in automated theorem proving use Large Language Models (LLMs) to translate informal mathematical statements into formal proofs. However, informal cues are often amb…
MetaReflection: Learning Instructions for Language Agents using Past Reflections
Priyanshu Gupta, Shashank Kirtania, Ananya Singha +4
The popularity of Large Language Models (LLMs) have unleashed a new age ofLanguage Agents for solving a diverse range of tasks. While contemporary frontier LLMs are capable enough…
LOGIC-LM++: Multi-Step Refinement for Symbolic Formulations
Shashank Kirtania, Priyanshu Gupta, Arjun Radhakirshna
In this paper we examine the limitations of Large Language Models (LLMs) for complex reasoning tasks. Although recent works have started to employ formal languages as an intermedia…