aiqbench analysis
This study reveals how decoder-only transformers resolve lexical ambiguity by showing that semantic distinctions remain active in middle layers but become increasingly invisible to standard similarity metrics like cosine distance in late layers. The findings provide causal evidence via activation patching, indicating that final-layer representational reconvergence does not equate to a loss of behavioral disambiguation capability across different model sizes. These insights are critical for developers relying on embedding-based methods such as semantic search and retrieval, warning them that high vector similarity in output layers may poorly correlate with actual token prediction divergence.
Divergent large language model predictions from convergent representations in ambiguous word pairs
arXiv:2608.01816v1 Announce Type: new Abstract: In this work we investigate how decoder-only transformers resolve lexical ambiguity through layer-by-layer analysis of three models spanning three parameter sizes (GPT-2-Small-117M, Llama-3.2-3B, Qwen2.5-32B). For both homonyms and polysemes, we find that representations become maximally distinct in middle layers, then partially reconverge in late layers, while the KL divergence between their next-token predictions reaches its maximum in the final layers. The activation patching experiment provides causal evidence that late-layer representational differences directly determine outputs despite apparent increased similarity in embedding space. Our single-layer ablation experiment indicates that models achieve equivalent disambiguation despite qualitatively different layer-wise vulnerabilities. These findings offer a mechanism for recent observations where models' internal embedding similarities show low correlation with their behavioural outputs despite strong performance. The semantic distinctions therefore remain present but become increasingly invisible to similarity measures over the embeddings, with implications for embedding-based methods such as semantic search, retrieval, and clustering that rely on late-layer cosine similarity.
Abstract:In this work we investigate how decoder-only transformers resolve lexical ambiguity through layer-by-layer analysis of three models spanning three parameter sizes (GPT-2-Small-117M, Llama-3.2-3B, Qwen2.5-32B). For both homonyms and polysemes, we find that representations become maximally distinct in middle layers, then partially reconverge in late layers, while the KL divergence between their next-token predictions reaches its maximum in the final layers. The activation patching experiment provides causal evidence that late-layer representational differences directly determine outputs despite apparent increased similarity in embedding space. Our single-layer ablation experiment indicates that models achieve equivalent disambiguation despite qualitatively different layer-wise vulnerabilities. These findings offer a mechanism for recent observations where models' internal embedding similarities show low correlation with their behavioural outputs despite strong performance. The semantic distinctions therefore remain present but become increasingly invisible to similarity measures over the embeddings, with implications for embedding-based methods such as semantic search, retrieval, and clustering that rely on late-layer cosine similarity.
Computation and Language (cs.CL)
arXiv:2608.01816 [cs.CL]
(or arXiv:2608.01816v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2608.01816 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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