Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
140 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
46 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

GraphInstruct: Empowering Large Language Models with Graph Understanding and Reasoning Capability (2403.04483v2)

Published 7 Mar 2024 in cs.AI and cs.CL

Abstract: Evaluating and enhancing the general capabilities of LLMs has been an important research topic. Graph is a common data structure in the real world, and understanding graph data is a crucial part for advancing general intelligence. To evaluate and enhance the graph understanding abilities of LLMs, in this paper, we propose a benchmark named GraphInstruct, which comprehensively includes 21 classical graph reasoning tasks, providing diverse graph generation pipelines and detailed reasoning steps. Based on GraphInstruct, we further construct GraphLM through efficient instruction-tuning, which shows prominent graph understanding capability. In order to enhance the LLM with graph reasoning capability as well, we propose a step mask training strategy, and construct a model named GraphLM+. As one of the pioneering efforts to enhance the graph understanding and reasoning abilities of LLMs, extensive experiments have demonstrated the superiority of GraphLM and GraphLM+ over other LLMs. We look forward to more researchers exploring the potential of LLMs in the graph data mining domain through GraphInstruct. Our code for generating GraphInstruct is released publicly at: https://github.com/CGCL-codes/GraphInstruct.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (31)
  1. Flamingo: a visual language model for few-shot learning. In Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, NeurIPS 2022, New Orleans, LA, USA, November 28 - December 9, 2022.
  2. Tallrec: An effective and efficient tuning framework to align large language model with recommendation. In Proceedings of the 17th ACM Conference on Recommender Systems, RecSys ’23, page 1007–1014, New York, NY, USA. Association for Computing Machinery.
  3. Albert-László Barabási and Réka Albert. 1999. Emergence of scaling in random networks. Science, 286(5439):509–512.
  4. Graphllm: Boosting graph reasoning ability of large language model. CoRR, abs/2310.05845.
  5. Exploring the potential of large language models (llms) in learning on graphs. CoRR, abs/2307.03393.
  6. Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality. See https://vicuna. lmsys. org (accessed 14 April 2023).
  7. Chatlaw: Open-source legal large language model with integrated external knowledge bases.
  8. Talk like a graph: Encoding graphs for large language models. CoRR, abs/2310.04560.
  9. Gpt4graph: Can large language models understand graph structured data ? an empirical evaluation and benchmarking. CoRR, abs/2305.15066.
  10. Explanations as features: Llm-based features for text-attributed graphs. CoRR, abs/2305.19523.
  11. Lora: Low-rank adaptation of large language models. In The Tenth International Conference on Learning Representations, ICLR 2022, Virtual Event, April 25-29, 2022. OpenReview.net.
  12. Audiogpt: Understanding and generating speech, music, sound, and talking head. CoRR, abs/2304.12995.
  13. Glen Jeh and Jennifer Widom. 2003. Scaling personalized web search. In Proceedings of the Twelfth International World Wide Web Conference, WWW 2003, Budapest, Hungary, May 20-24, 2003, pages 271–279. ACM.
  14. Mistral 7b. CoRR, abs/2310.06825.
  15. Diederik P. Kingma and Jimmy Ba. 2015. Adam: A method for stochastic optimization. In 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings.
  16. Large language models are zero-shot reasoners. In Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, NeurIPS 2022, New Orleans, LA, USA, November 28 - December 9, 2022.
  17. Ecomgpt: Instruction-tuning large language models with chain-of-task tasks for e-commerce. CoRR, abs/2308.06966.
  18. Chatdoctor: A medical chat model fine-tuned on a large language model meta-ai (llama) using medical domain knowledge. Cureus, 15(6).
  19. Wizardcoder: Empowering code large language models with evol-instruct. CoRR, abs/2306.08568.
  20. OpenAI. 2023. GPT-4 technical report. CoRR, abs/2303.08774.
  21. Training language models to follow instructions with human feedback. In Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, NeurIPS 2022, New Orleans, LA, USA, November 28 - December 9, 2022.
  22. Graphgpt: Graph instruction tuning for large language models.
  23. Llama 2: Open foundation and fine-tuned chat models. CoRR, abs/2307.09288.
  24. Huatuo: Tuning llama model with chinese medical knowledge.
  25. Can language models solve graph problems in natural language? In Thirty-seventh Conference on Neural Information Processing Systems.
  26. Duncan J Watts and Steven H Strogatz. 1998. Collective dynamics of ‘small-world’networks. nature, 393(6684):440–442.
  27. Chain-of-thought prompting elicits reasoning in large language models. In Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, NeurIPS 2022, New Orleans, LA, USA, November 28 - December 9, 2022.
  28. Bloomberggpt: A large language model for finance. CoRR, abs/2303.17564.
  29. Evaluating spatial understanding of large language models. CoRR, abs/2310.14540.
  30. GPT can solve mathematical problems without a calculator. CoRR, abs/2309.03241.
  31. Dyval: Graph-informed dynamic evaluation of large language models. In The Twelfth International Conference on Learning Representations.
Citations (21)
View on Semantic Scholar

Summary

  • The paper introduces the GraphInstruct benchmark featuring 21 classical graph reasoning tasks with detailed Chain-of-Thought style steps.
  • The paper presents GraphLM and GraphLM+ models fine-tuned with LoRA and step mask training to enhance graph structure understanding.
  • The paper’s experiments reveal that GraphLM outperforms models like Vicuna-7b, underscoring persistent challenges in complex graph reasoning.

The paper "GraphInstruct: Empowering LLMs with Graph Understanding and Reasoning Capability" introduces a novel approach to enhance the graph understanding and reasoning capabilities of LLMs. Recognizing the limitations of current LLMs in handling graph-structured data, the authors present a benchmark named GraphInstruct. This benchmark is meticulously designed to include 21 classical graph reasoning tasks, such as node degree, connectivity, shortest path, and maximum flow, among others.

The primary contributions of this paper are organized as follows:

  1. GraphInstruct Benchmark:
    • The benchmark encompasses a comprehensive set of tasks covering node-level, node-pair-level, and graph-level reasoning challenges.
    • Each task is associated with detailed problem-solving steps, akin to the Chain-of-Thought method, aimed at enhancing the LLMs' problem-solving capabilities.
    • The diversity of graph structures in the benchmark is achieved through various graph generation methods like random networks, small-world networks, and BA scale-free networks. Graphs are objectified by different description languages and node ID representations to test model versatility.
  2. GraphLM and GraphLM+ Models:
    • GraphLM is developed through instruction-tuning on GraphInstruct using a fine-tuning strategy with LoRA, enhancing the graph understanding capability of the base model Vicuna-7b.
    • GraphLM+ further builds on GraphLM by incorporating intermediate reasoning steps as supervision signals. A step mask training strategy filters redundant information to focus on relevant graph structure information, thereby bolstering graph reasoning capabilities.
  3. Experimental Evaluation:
    • GraphLM notably outperforms other LLMs like Vicuna-7b and exhibits performance comparable to GPT-3.5-turbo across various graph reasoning tasks.
    • Despite advancements, both GraphLM and GPT4 show weaknesses in complex graph reasoning tasks, manifesting the existing difficulty in LLMs' comprehension of such data.
    • The paper details extensive experiments demonstrating GraphLM+'s superior ability to handle tasks using the one-shot Chain-of-Thought technique, reflecting significantly improved reasoning capabilities.
    • The generalization of GraphLM's performance was assessed in diverse settings involving different graph sizes, description languages, and node ID representations, where it consistently maintained a performance edge.

In conclusion, the paper advances the understanding of LLMs in graph domains by systematically exploring the graph-structured data comprehension challenges, providing a robust benchmark, and detailing novel model architectures. It opens pathways for further exploration into the interplay between LLMs and graph data mining tasks, aiming to bridge the gap in LLM capabilities regarding graph reasoning. Future directions include expanding the diversity of GraphInstruct tasks to real-world applications and further enhancing LLM integration with graphs.

File Document Download Save Streamline Icon: https://streamlinehq.com PDF File Document Download Save Streamline Icon: https://streamlinehq.com Markdown
Youtube Logo Streamline Icon: https://streamlinehq.com

YouTube