6 papers
BODHI: Do LLMs Branch Out and Discover Heterogeneous Inferences?
Soumadeep Saha, Krish Sharma, Akshay Chaturvedi +1
Although reinforcement learning with verifiable rewards (RLVR) has improved the performance of large language models (LLMs) across a variety of reasoning tasks, there is significan…
KisMATH: Do LLMs Have Knowledge of Implicit Structures in Mathematical Reasoning?
Soumadeep Saha, Akshay Chaturvedi, Saptarshi Saha +2
Chain-of-thought (CoT) traces have been shown to improve performance of large language models on a plethora of reasoning tasks, yet there is no consensus on the mechanism by which…
DIMSUM: Discourse in Mathematical Reasoning as a Supervision Module
Krish Sharma, Niyar R Barman, Akshay Chaturvedi +1
We look at reasoning on GSM8k, a dataset of short texts presenting primary school, math problems. We find, with Mirzadeh et al. (2024), that current LLM progress on the data set ma…
Learning Semantic Structure through First-Order-Logic Translation
Akshay Chaturvedi, Nicholas Asher
In this paper, we study whether transformer-based language models can extract predicate argument structure from simple sentences. We firstly show that language models sometimes con…
Llamipa: An Incremental Discourse Parser
Kate Thompson, Akshay Chaturvedi, Julie Hunter +1
This paper provides the first discourse parsing experiments with a large language model(LLM) finetuned on corpora annotated in the style of SDRT (Segmented Discourse Representation…
Nebula: A discourse aware Minecraft Builder
Akshay Chaturvedi, Kate Thompson, Nicholas Asher
When engaging in collaborative tasks, humans efficiently exploit the semantic structure of a conversation to optimize verbal and nonverbal interactions. But in recent "language to…