Difference between revisions of "Increasing AI Intelligence"

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=Reviews=
 
=Reviews=
 
* 2024-12: [https://arxiv.org/abs/2412.11936 A Survey of Mathematical Reasoning in the Era of Multimodal Large Language Model: Benchmark, Method & Challenges]
 
* 2024-12: [https://arxiv.org/abs/2412.11936 A Survey of Mathematical Reasoning in the Era of Multimodal Large Language Model: Benchmark, Method & Challenges]
 +
* 2025-01: [https://arxiv.org/abs/2501.02497 Test-time Computing: from System-1 Thinking to System-2 Thinking] ([https://github.com/Dereck0602/Awesome_Test_Time_LLMs github list of papers])
 
* Links to papers: [https://github.com/hijkzzz/Awesome-LLM-Strawberry Awesome LLM Strawberry (OpenAI o1)]
 
* Links to papers: [https://github.com/hijkzzz/Awesome-LLM-Strawberry Awesome LLM Strawberry (OpenAI o1)]
  
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'''Review'''
 
'''Review'''
 
* 2024-06: [https://arxiv.org/abs/2406.16838 From Decoding to Meta-Generation: Inference-time Algorithms for Large Language Models]
 
* 2024-06: [https://arxiv.org/abs/2406.16838 From Decoding to Meta-Generation: Inference-time Algorithms for Large Language Models]
 +
* 2025-01: [https://arxiv.org/abs/2501.02497 Test-time Computing: from System-1 Thinking to System-2 Thinking] ([https://github.com/Dereck0602/Awesome_Test_Time_LLMs github list of papers])
  
 
===In context learning (ICL), search, and other inference-time methods===
 
===In context learning (ICL), search, and other inference-time methods===

Latest revision as of 10:28, 7 January 2025

Reviews

Prompt Engineering

Fine Tuning

Proactive Search

Compute expended after training, but before inference.

Training Data (Data Refinement, Synthetic Data)

Generate consistent plans/thoughts

  • 2024-08: Mutual Reasoning Makes Smaller LLMs Stronger Problem-Solvers (code)
    • (Microsoft) rStar is a self-play mutual reasoning approach. A small model adds to MCTS using some defined reasoning heuristics. Mutually consistent trajectories can be emphasized.
  • 2024-09: Self-Harmonized Chain of Thought
    • Produce refined chain-of-thought style solutions/prompts for diverse problems. Given a large set of problems/questions, first aggregated semantically, then apply zero-shot chain-of-thought to each problem. Then cross-pollinate between proposed solutions to similar problems, looking for refined and generalize solutions.
  • 2024-11: LLMs Do Not Think Step-by-step In Implicit Reasoning
    • They argue that models trained to reproduce CoT outputs do not, internally, perform stepwise reasoning (with intermediate representations); this suggests that explicit CoT could be superior to implicit CoT.

Sampling

Automated prompt generation

Distill inference-time-compute into model

CoT reasoning model

Scaling

Inference Time Compute

Methods

Review

In context learning (ICL), search, and other inference-time methods

Inference-time Sampling

Inference-time Gradient

Self-prompting

Retrieval or Memory

In-context thought

Naive multi-LLM (verification, majority voting, best-of-N, etc.)

Multi-LLM (multiple comparisons, branching, etc.)

Iteration (e.g. neural-like layered blocks)

Iterative reasoning via graphs

Monte Carlo Tree Search (MCTS)

Other Search

Chain-of-Thought Reasoning

Analysis

Scaling

Theory

Expending compute works

Compute.png

Pitfalls

Pragmatics

Code for Inference-time Compute

  • optillm: Inference proxy which implements state-of-the-art techniques to improve accuracy and performance of LLMs (improve reasoning over coding, logical and mathematical queries)

Memory

Tool Use

Integrated

Multi-agent Effort (and Emergent Intelligence)

ML-like Optimization of LLM Setup

See Also

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