LongProc: Benchmarking Long-Context Language Models on Long Procedural Generation
LongProc benchmarks long-context language models on long procedural generation, revealing current model limitations.
Key Findings
Methodology
LongProc benchmark includes six diverse procedural generation tasks requiring models to integrate dispersed information and generate long-form outputs. Tasks include extracting structured information from HTML pages into TSV format and executing complex search procedures to create travel plans. All tasks adhere to deterministic procedures and produce structured outputs, allowing reliable rule-based evaluation.
Key Results
- In evaluating 23 LCLMs, despite all models claiming a context window size above 32K tokens, open-weight models typically falter on 2K-token tasks, and closed-source models like GPT-4o show significant degradation on 8K-token tasks.
- Reasoning models achieve stronger overall performance in long-form generation, benefiting from long CoT training.
- Analysis reveals LCLMs struggle to maintain long-range coherence in long-form generations.
Significance
LongProc provides a new evaluation framework, revealing critical limitations in current long-context language models on long procedural generation tasks. These findings are significant for academia and industry, especially in applications requiring the integration of large amounts of dispersed information.
Technical Contribution
LongProc expands the scope of existing benchmarks by introducing diverse procedural generation tasks, providing a more comprehensive evaluation of LCLMs' performance in long-form generation tasks. Its structured outputs and deterministic procedures make evaluation more reliable.
Novelty
LongProc is the first to combine long-form generation with dispersed information integration, offering a new evaluation framework that fills the gap in existing benchmarks for long-form generation tasks.
Limitations
- Current models perform poorly on 8K-token tasks, especially open-weight models.
- Models exhibit significant deficiencies in maintaining long-range coherence.
- Evaluation is limited to structured outputs and may not apply to open-ended generation tasks.
Future Work
Future research could explore improving models' coherence and accuracy in long-form generation, developing new algorithms to better handle dispersed information integration and generation.
AI Executive Summary
Long-context language models (LCLMs) have made significant advances in recent years, expanding the capacity of context windows. However, existing benchmarks primarily focus on long-context recall, requiring models to generate short responses based on a few critical snippets while processing thousands of irrelevant tokens. To address this limitation, researchers introduce LongProc, a new benchmark requiring models to integrate highly dispersed information and perform long-form generation. LongProc consists of six diverse procedural generation tasks, such as extracting structured information from HTML pages into TSV format and executing complex search procedures to create travel plans. These tasks challenge LCLMs by testing their ability to follow detailed procedural instructions, synthesize and reason over dispersed information, and generate structured, long-form outputs.
In evaluating 23 LCLMs, including instruction-tuned models and recent reasoning models, researchers tested at three difficulty levels, with the maximum number of output tokens set at 500, 2K, and 8K. Notably, while all tested models claim a context window size above 32K tokens, open-weight models typically falter on 2K-token tasks, and closed-source models like GPT-4o show significant degradation on 8K-token tasks. Reasoning models achieve stronger overall performance in long-form generation, benefiting from long CoT training.
These findings reveal critical limitations in current LCLMs on long-form generation tasks and suggest substantial room for improvement. LongProc serves as a new testbed for evaluating LCLMs, emphasizing future research directions in long-context modeling.
Deep Analysis
Background
Long-context language models (LCLMs) have made significant advances in recent years, expanding the capacity of context windows. This progress is largely due to advancements in pre-training, architectures, and data engineering methods. However, existing benchmarks primarily focus on long-context recall, requiring models to generate short responses based on a few critical snippets while processing thousands of irrelevant tokens. This approach provides limited insight into LCLMs' performance on more practical long-context tasks that require the integration of dispersed information and long-form generation.
Core Problem
The limitation of existing benchmarks is that they typically only test low-dispersion scenarios, requiring LCLMs to locate and use a few relevant snippets within the long contexts. This approach fails to adequately assess LCLMs' performance on more practical long-context tasks that require the integration of dispersed information and long-form generation.
Innovation
LongProc expands the scope of existing benchmarks by introducing diverse procedural generation tasks. Each task requires models to follow specified procedures and generate structured outputs. These tasks include extracting target information from lengthy unstructured HTML documents into structured TSV files, or creating trip plans by executing prolonged search procedures. Through these tasks, LongProc provides a more comprehensive evaluation of LCLMs' performance in long-form generation tasks.
Methodology
- �� LongProc benchmark includes six diverse procedural generation tasks.
- �� Each task requires models to integrate dispersed information and generate long-form outputs.
- �� Tasks include extracting structured information from HTML pages into TSV format and executing complex search procedures to create travel plans.
- �� All tasks adhere to deterministic procedures and produce structured outputs, allowing reliable rule-based evaluation.
Experiments
Researchers evaluated 23 LCLMs, including instruction-tuned models and recent reasoning models. Tests were conducted at three difficulty levels, with the maximum number of output tokens set at 500, 2K, and 8K. Despite all tested models claiming a context window size above 32K tokens, open-weight models typically falter on 2K-token tasks, and closed-source models like GPT-4o show significant degradation on 8K-token tasks.
Results
Reasoning models achieve stronger overall performance in long-form generation, benefiting from long CoT training. Analysis reveals LCLMs struggle to maintain long-range coherence in long-form generations. These findings reveal critical limitations in current LCLMs on long-form generation tasks and suggest substantial room for improvement.
Applications
LongProc serves as a new testbed for evaluating LCLMs, emphasizing future research directions in long-context modeling. These tasks have direct practical significance for applications requiring the integration of large amounts of dispersed information.
Limitations & Outlook
While LongProc provides a new evaluation framework, current models perform poorly on 8K-token tasks, especially open-weight models. Additionally, models exhibit significant deficiencies in maintaining long-range coherence. Evaluation is limited to structured outputs and may not apply to open-ended generation tasks.
Plain Language Accessible to non-experts
Imagine you're in a kitchen making a complex dish. You need to gather various ingredients from different cabinets and the fridge, and follow the recipe's steps. Each step requires you to measure and mix ingredients accurately, ultimately presenting a complete dish. Long-context language models are like this process, needing to extract relevant parts from a large amount of information and generate a complete output according to a specific procedure. Just like in the kitchen, the model needs to maintain accuracy and consistency at each step to ensure the final result is correct.
ELI14 Explained like you're 14
Imagine you're playing a massive multiplayer online game, where you need to collect clues from different quests to solve a big puzzle. Each quest gives you some information, and you need to piece it all together to find the path to victory. Long-context language models are like this game; they need to find key parts from a lot of information and then generate a complete answer according to specific steps. Just like in the game, the model needs to stay focused and accurate at each step to ensure the final victory.
Glossary
Long-context language model (LCLM)
A language model capable of processing and generating long texts.
Used to evaluate model performance on long procedural generation tasks.
Procedural generation
The process of generating structured outputs according to a given procedure.
Core task in the LongProc benchmark.
Deterministic procedure
A procedure that always produces the same output for the same input.
Foundation of LongProc tasks, allowing reliable evaluation.
Structured output
Information output organized in a specific format.
Required output form for LongProc tasks.
Reasoning model
A language model trained to enhance reasoning capabilities.
Performs better in long-form generation tasks.
Open Questions Unanswered questions from this research
- 1 How to improve model coherence in long-form generation?
- 2 How to enhance model accuracy when handling dispersed information?
Applications
Immediate Applications
Information extraction
Extract structured information from complex web pages, applicable to data analysis and business intelligence.
Long-term Vision
Automated planning
Automatically generate plans in travel and logistics, reducing human intervention.
Abstract
Existing benchmarks for evaluating long-context language models (LCLMs) primarily focus on long-context recall, requiring models to produce short responses based on a few critical snippets while processing thousands of irrelevant tokens. We introduce LongProc (Long Procedural Generation), a new benchmark that requires both the integration of highly dispersed information and long-form generation. LongProc consists of six diverse procedural generation tasks, such as extracting structured information from HTML pages into a TSV format and executing complex search procedures to create travel plans. These tasks challenge LCLMs by testing their ability to follow detailed procedural instructions, synthesize and reason over dispersed information, and generate structured, long-form outputs (up to 8K tokens). Furthermore, as these tasks adhere to deterministic procedures and yield structured outputs, they enable reliable rule-based evaluation. We evaluated 23 LCLMs, including instruction-tuned models and recent reasoning models, on LongProc at three difficulty levels, with the maximum number of output tokens set at 500, 2K, and 8K. Notably, while all tested models claim a context window size above 32K tokens, open-weight models typically falter on 2K-token tasks, and closed-source models like GPT-4o show significant degradation on 8K-token tasks. Reasoning models achieve stronger overall performance in long-form generation, benefiting from long CoT training. Further analysis reveals that LCLMs struggle to maintain long-range coherence in long-form generations. These findings highlight critical limitations in current LCLMs and suggest substantial room for improvement. Data and code available at: https://princeton-pli.github.io/LongProc.