Not All LLM Reasoners Are Created Equal
We study the depth of grade-school math (GSM) problem-solving capabilities of
LLMs. To this end, we evaluate their performance on pairs of existing math word
problems together so that the answer to the second problem depends on correctly
answering the first problem. Our findings reveal a significant reasoning gap in
most LLMs, that is performance difference between solving the compositional
pairs and solving each question independently. This gap is more pronounced in
smaller, more cost-efficient, and math-specialized models. Moreover,
instruction-tuning recipes and code generation have varying effects across LLM
sizes, while finetuning on GSM can lead to task overfitting. Our analysis
indicates that large reasoning gaps are not because of test-set leakage, but
due to distraction from additional context and poor second-hop reasoning.
Overall, LLMs exhibit systematic differences in their reasoning abilities,
despite what their performance on standard benchmarks indicates.
66fe4d83b7c3b3c0f298f78b
Arian Hosseini
extracted_pending
2024-10-03T07:53:39.980Z
62e447e9640206c5d21e5ff6
/avatars/42d8247c2ffa98a4c6255ce84051f1b9.svg
Arian Hosseini
user
arianhosseini
66fe4d83b7c3b3c0f298f78c
Alessandro Sordoni
66fe4d83b7c3b3c0f298f78d
Daniel Toyama
66fe4d83b7c3b3c0f298f78e
Aaron Courville
66fe4d83b7c3b3c0f298f78f
Rishabh Agarwal
extracted_pending
2024-10-03T07:53:39.980Z
647875e37b370854241b89a8
/avatars/fd25f7e5960e4e132b9ee689f114820b.svg
Rishabh Agarwal
user
agarwl
Not All LLM Reasoners Are Created Equal
We study the depth of grade-school math (GSM) problem-solving capabilities of
LLMs. To this end, we evaluate their performance on pairs of existing math word
problems together so that the answer to the second problem depends on correctly
answering the first problem. Our findings reveal a significant reasoning gap in
most LLMs, that is performance difference between solving the compositional
pairs and solving each question independently. This gap is more pronounced in
smaller, more cost-efficient, and math-specialized models. Moreover,
instruction-tuning recipes and code generation have varying effects across LLM
sizes, while finetuning on GSM can lead to task overfitting. Our analysis
indicates that large reasoning gaps are not because of test-set leakage, but
due to distraction from additional context and poor second-hop reasoning.
Overall, LLMs exhibit systematic differences in their reasoning abilities,
despite what their performance on standard benchmarks indicates.
LLMs display a significant reasoning gap when solving dependent math problems, which is more pronounced in smaller and math-specialized models, and is attributed to distraction from additional context and poor second-hop reasoning.