1 citations · 2 across the 13 of their papers we have counts for
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Scaling Competence, Shrinking Reasoning: Cognitive Signatures in Language Model Learning
Mukul Singh, Ananya Singha, Arjun Radhakrishna +1
We analyze reasoning in language models during task-specific fine-tuning and draws parallel between reasoning tokens--intermediate steps generated while solving problem and the hum…
ConDABench: Interactive Evaluation of Language Models for Data Analysis
Avik Dutta, Priyanshu Gupta, Hosein Hasanbeig +6
Real-world data analysis tasks often come with under-specified goals and unclean data. User interaction is necessary to understand and disambiguate a user's intent, and hence, esse…
TEN: Table Explicitization, Neurosymbolically
Nikita Mehrotra, Aayush Kumar, Sumit Gulwani +2
We present a neurosymbolic approach, TEN, for extracting tabular data from semistructured input text. This task is particularly challenging for text input that does not use special…
An Empirical Study of Validating Synthetic Data for Formula Generation
Usneek Singh, José Cambronero, Sumit Gulwani +5
Large language models (LLMs) can be leveraged to help with writing formulas in spreadsheets, but resources on these formulas are scarce, impacting both the base performance of pre-…
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…
Enhancing Creativity in Large Language Models through Associative Thinking Strategies
Pronita Mehrotra, Aishni Parab, Sumit Gulwani
This paper explores the enhancement of creativity in Large Language Models (LLMs) like vGPT-4 through associative thinking, a cognitive process where creative ideas emerge from lin…