Struc-Bench: Are Large Language Models Really Good at Generating Complex
  Structured Data?
Despite the power of Large Language Models (LLMs) like GPT-4, they still
struggle with tasks that require generating complex, structured outputs. In
this study, we assess the capability of Current LLMs in generating complex
structured data and propose a structure-aware fine-tuning approach as a
solution to improve this ability. To perform a comprehensive evaluation, we
propose Struc-Bench, include five representative LLMs (i.e., GPT-NeoX 20B,
GPT-3.5, GPT-4, and Vicuna) and evaluate them on our carefully constructed
datasets spanning raw text, HTML, and LaTeX tables. Based on our analysis of
current model performance, we identify specific common formatting errors and
areas of potential improvement. To address complex formatting requirements, we
utilize FormatCoT (Chain-of-Thought) to generate format instructions from
target outputs. Our experiments show that our structure-aware fine-tuning
method, when applied to LLaMA-7B, significantly improves adherence to natural
language constraints, outperforming other evaluated LLMs. Based on these
results, we present an ability map of model capabilities from six dimensions
(i.e., coverage, formatting, reasoning, comprehension, pragmatics, and
hallucination). This map highlights the weaknesses of LLMs in handling complex
structured outputs and suggests promising directions for future work. Our code
and models can be found at https://github.com/gersteinlab/Struc-Bench.
65090cabe64ee37323aef98e
Xiangru Tang
claimed_verified
2024-02-07T11:18:12.620Z
63357c608adfa81faf2ac180
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Xiangru Tang
user
RTT1
65090cabe64ee37323aef98f
Yiming Zong
65090cabe64ee37323aef990
Yilun Zhao
admin_assigned
2023-09-20T14:39:37.084Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/62f662bcc58915315c4eccea/zOAQLONfMP88zr70sxHK-.jpeg
Yilun Zhao
user
yilunzhao
65090cabe64ee37323aef991
Arman Cohan
admin_assigned
2023-09-20T14:39:45.163Z
5f5ba21188f57f65f951f255
https://cdn-avatars.huggingface.co/v1/production/uploads/1599840760465-noauth.png
Arman Cohan
user
armanc
65090cabe64ee37323aef992
Mark Gerstein
Struc-Bench: Are Large Language Models Really Good at Generating Complex
  Structured Data?
Despite the power of Large Language Models (LLMs) like GPT-4, they still
struggle with tasks that require generating complex, structured outputs. In
this study, we assess the capability of Current LLMs in generating complex
structured data and propose a structure-aware fine-tuning approach as a
solution to improve this ability. To perform a comprehensive evaluation, we
propose Struc-Bench, include five representative LLMs (i.e., GPT-NeoX 20B,
GPT-3.5, GPT-4, and Vicuna) and evaluate them on our carefully constructed
datasets spanning raw text, HTML, and LaTeX tables. Based on our analysis of
current model performance, we identify specific common formatting errors and
areas of potential improvement. To address complex formatting requirements, we
utilize FormatCoT (Chain-of-Thought) to generate format instructions from
target outputs. Our experiments show that our structure-aware fine-tuning
method, when applied to LLaMA-7B, significantly improves adherence to natural
language constraints, outperforming other evaluated LLMs. Based on these
results, we present an ability map of model capabilities from six dimensions
(i.e., coverage, formatting, reasoning, comprehension, pragmatics, and
hallucination). This map highlights the weaknesses of LLMs in handling complex
structured outputs and suggests promising directions for future work. Our code
and models can be found at https://github.com/gersteinlab/Struc-Bench.
Structure-aware fine-tuning improves Large Language Models' ability to generate complex structured data by reducing formatting errors and enhancing adherence to natural language constraints.
Large Language Models structure-aware fine-tuning Struc-Bench GPT-NeoX 20B GPT-3.5 GPT-4 Vicuna FormatCoT Chain-of-Thought LLaMA-7B ability map coverage formatting reasoning comprehension pragmatics hallucination