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Forking Paths in Neural Text Generation
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Proposes the Forking Tokens Hypothesis and Forking Paths Analysis, a black-box method that resamples GPT-3.5 at alternate tokens and uses change point detection and survival analysis to find tokens that sharply shift final outcomes.
Problem
Existing LLM uncertainty estimation focuses on final answers and ignores intermediate tokens that can determine the outcome of generation; the paper asks whether individual tokens can fork generation into very different outcomes.
Approach
Model per-token outcome distributions over resampled continuations as time series and conditional distributions, then apply Bayesian change point detection (BEAST) and discrete-time survival analysis to detect forking tokens, without fine-tuning or weight access.
Key results
- Change points detected with 90% confidence in 63% of LastLetter and 43% of MMLU question/answer pairs, versus 0% for CoinFlip.
- Mean end-of-sequence survival S(T) at epsilon=0.6 is as low as 0.13 for MMLU and 0.18 for GSM8k.
- In HotpotQA-8076, the outcome distribution collapses from the correct answer to a hallucinated answer at a single forking token, while static baselines assign near-100% confidence to the wrong answer.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use 10–20 samples per token instead of S=30 (ablation) | Forking Paths Analysis · Analyzed tasks | estimated number of change points: 10-20 samples; S=30 | No clear effect |
| Use L1 distance or K-L divergence instead of L2 distance (ablation) | Forking Paths Analysis · Analyzed tasks | agreement with qualitative judgments | Hurt |
| Use token-level Forking Paths Analysis instead of static uncertainty estimates | GPT-3.5 uncertainty estimation · HotpotQA-8076 | visibility of uncertainty dynamics | Helped |
| Use token-level Forking Paths Analysis instead of static uncertainty estimates | GPT-3.5 uncertainty estimation · GSM8K-59 | visibility of uncertainty dynamics | Helped |
Surprising findings
- Forking tokens often occur at innocuous tokens such as open parentheses and punctuation or space characters rather than semantically key words.
- Token logit probability is not strongly predictive of change points; there is a slight positive correlation.
Limitations
- Very costly in sampled tokens, scaling with base path length, alternate tokens, and samples per branch.
- Relies on a second LLM (Gemini Flash) for answer extraction, and semantic variation in open-ended outputs is hard to control.
- Change point detection shows high false positive rates when the outcome drift is small.
Experimental setup
Datasets: CoinFlip LastLetter AQuA GSM8k MMLU HotpotQA StoryCloze
Benchmarks: CoinFlip LastLetter AQuA GSM8k MMLU HotpotQA StoryCloze
Metrics: change point count (posterior p(m|y)) change point time distribution survival rate S(T)
Baselines: static resampling uncertainty final-answer token logit probability verbalized confidence
Builds on: chain-of-thought prompting BEAST Bayesian change point detection
Closest papers by meaning
- Improving Uncertainty Estimation through Semantically Diverse Language Generation (ICLR 2025 · similarity 0.91)
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- Distinguishing the Knowable from the Unknowable with Language Models (ICML 2024 · similarity 0.89)
- SPANUQ: Span-Level Uncertainty Quantification for Large Language Model Generation (NeurIPS 2026 · similarity 0.89)
- Mapping from Meaning: Addressing the Miscalibration of Prompt-Sensitive Language Models (AAAI 2025 · similarity 0.88)
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