5 papers
Exploring Continual Fine-Tuning for Enhancing Language Ability in Large Language Model
Divyanshu Aggarwal, Sankarshan Damle, Navin Goyal +2
A common challenge towards the adaptability of Large Language Models (LLMs) is their ability to learn new languages over time without hampering the model's performance on languages…
Chow-Liu Ordering for Long-Context Reasoning in Chain-of-Agents
Naman Gupta, Vaibhav Singh, Arun Iyer +8
Sequential multi-agent reasoning frameworks such as Chain-of-Agents (CoA) handle long-context queries by decomposing inputs into chunks and processing them sequentially using LLM-b…
Truthful Reverse Auctions for Adaptive Selection via Contextual Multi-Armed Bandits
Pronoy Patra, Sankarshan Damle, Manisha Padala +1
We study the problem of selecting large language models (LLMs) for user queries in settings where multiple LLM providers submit the cost of solving a query. From the users' perspec…
COSMIR: Chain Orchestrated Structured Memory for Iterative Reasoning over Long Context
Naman Gupta, Shreeyash Gowaikar, Arun Iyer +6
Reasoning over very long inputs remains difficult for large language models (LLMs). Common workarounds either shrink the input via retrieval (risking missed evidence), enlarge the…
LLMs for Resource Allocation: A Participatory Budgeting Approach to Inferring Preferences
Sankarshan Damle, Boi Faltings
Large Language Models (LLMs) are increasingly expected to handle complex decision-making tasks, yet their ability to perform structured resource allocation remains underexplored. E…