A review of Agentic bemchmarks for LLMs. What are the challenges for current gen algorithms?
In Short
AgentBench and SmartPlay are leading benchmarks for evaluating LLMs as agents, assessing reasoning, decision-making, and instruction following abilities. Key challenges include poor long-term reasoning, limited decision-making, and instruction following capabilities, especially for open-source models compared to top commercial LLMs.
Overview of agentic benchmarks for LLMs
Key benchmarks
- AgentBench: A multi-dimensional evolving benchmark with 8 distinct environments to assess LLM agents' capabilities in multi-turn open-ended generation settings
- SmartPlay: Consists of 6 games with 20+ evaluation settings and infinite environment variations, challenging 9 important capabilities of LLM agents
- LLM-Coordination Benchmark: Evaluates LLMs in multi-agent coordination through Agentic Coordination and Coordination Question Answering tasks
- LMRL-Gym: A benchmark for evaluating multi-turn reinforcement learning with LLMs, featuring 8 language tasks requiring multiple rounds of interaction
- GoodAI LTM Benchmark: Focuses on evaluating long-term memory and continual learning capabilities of LLM-based conversational agents
Capabilities assessed
- Reasoning with object dependencies
- Planning ahead
- Spatial reasoning
- Learning from history
- Understanding randomness
- Theory of Mind (ToM) reasoning
- Joint planning
- Instruction following
- Long-term memory management
Challenges for current gen algorithms
Reasoning and decision-making limitations
- Poor long-term reasoning: LLMs struggle with tasks requiring extended logical chains or maintaining context over long periods
- Limited decision-making abilities: Difficulty in making optimal choices in complex, multi-step scenarios
- Struggles with spatial reasoning: Challenges in understanding and manipulating spatial relationships in environments like Minecraft
- Difficulty in understanding randomness: LLMs often fail to grasp probabilistic concepts fully
Instruction following and task execution
- Weak instruction following abilities: Especially evident in open-source models compared to top commercial LLMs
- Challenges in multi-turn interactions: LLMs rarely ask clarifying questions or engage in explicit information gathering
- Inefficient task decomposition: Difficulty in breaking down complex tasks into manageable subtasks
Memory and learning challenges
- Limited context window: Restricts the ability to process long documents or maintain context over extended conversations
- Ineffective long-term memory management: Struggles with retaining and utilizing information over extended periods
- Challenges in continual learning: Difficulty in integrating new information and adapting strategies over time
Multi-agent coordination issues
- Weak Theory of Mind reasoning: Difficulty in understanding and predicting other agents' intentions and knowledge states
- Joint planning limitations: Struggles in coordinating actions with other agents towards a common goal
- Challenges in adapting to dynamic environments: Difficulty in adjusting strategies based on changing conditions or partner behaviors
Benchmark-specific challenges
- Reproducibility issues: Non-reproducible model scores and arena rankings due to changing API-served models
- Cost-related performance discrepancies: Expensive models may outperform others due to multiple API calls rather than superior architecture
- Overfitting to specific benchmarks: Risk of models being optimized for benchmark performance rather than real-world applications
Potential improvements and future directions
- Training on code and high-quality multi-turn alignment data: Could enhance long-term reasoning and decision-making capabilities
- Implementing more advanced planning techniques: To improve performance on open-ended or ambiguous tasks
- Developing better memory architectures: To enhance long-term information retention and retrieval
- Improving instruction following through fine-tuning: To bridge the gap between open-source and commercial models
- Enhancing multi-agent coordination abilities: Through specialized training in Theory of Mind and joint planning scenarios