Mixing Mechanisms: How Language Models Retrieve Bound Entities
  In-Context
A key component of in-context reasoning is the ability of language models
(LMs) to bind entities for later retrieval. For example, an LM might represent
"Ann loves pie" by binding "Ann" to "pie", allowing it to later retrieve "Ann"
when asked "Who loves pie?" Prior research on short lists of bound entities
found strong evidence that LMs implement such retrieval via a positional
mechanism, where "Ann" is retrieved based on its position in context. In this
work, we find that this mechanism generalizes poorly to more complex settings;
as the number of bound entities in context increases, the positional mechanism
becomes noisy and unreliable in middle positions. To compensate for this, we
find that LMs supplement the positional mechanism with a lexical mechanism
(retrieving "Ann" using its bound counterpart "pie") and a reflexive mechanism
(retrieving "Ann" through a direct pointer). Through extensive experiments on
nine models and ten binding tasks, we uncover a consistent pattern in how LMs
mix these mechanisms to drive model behavior. We leverage these insights to
develop a causal model combining all three mechanisms that estimates next token
distributions with 95% agreement. Finally, we show that our model generalizes
to substantially longer inputs of open-ended text interleaved with entity
groups, further demonstrating the robustness of our findings in more natural
settings. Overall, our study establishes a more complete picture of how LMs
bind and retrieve entities in-context.
68e5e202975ac4c405ef212c
Yoav Gur-Arieh
claimed_verified
2025-10-08T05:49:18.548Z
621febb6c7f47c5eb5df001d
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Yoav Gur Arieh
user
yoavgur
68e5e202975ac4c405ef212d
Mor Geva
claimed_verified
2025-10-09T06:51:44.787Z
610b729f9da682cd54ad9adf
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Mor Geva
user
mega
68e5e202975ac4c405ef212e
Atticus Geiger
Mixing Mechanisms: How Language Models Retrieve Bound Entities
  In-Context
A key component of in-context reasoning is the ability of language models
(LMs) to bind entities for later retrieval. For example, an LM might represent
"Ann loves pie" by binding "Ann" to "pie", allowing it to later retrieve "Ann"
when asked "Who loves pie?" Prior research on short lists of bound entities
found strong evidence that LMs implement such retrieval via a positional
mechanism, where "Ann" is retrieved based on its position in context. In this
work, we find that this mechanism generalizes poorly to more complex settings;
as the number of bound entities in context increases, the positional mechanism
becomes noisy and unreliable in middle positions. To compensate for this, we
find that LMs supplement the positional mechanism with a lexical mechanism
(retrieving "Ann" using its bound counterpart "pie") and a reflexive mechanism
(retrieving "Ann" through a direct pointer). Through extensive experiments on
nine models and ten binding tasks, we uncover a consistent pattern in how LMs
mix these mechanisms to drive model behavior. We leverage these insights to
develop a causal model combining all three mechanisms that estimates next token
distributions with 95% agreement. Finally, we show that our model generalizes
to substantially longer inputs of open-ended text interleaved with entity
groups, further demonstrating the robustness of our findings in more natural
settings. Overall, our study establishes a more complete picture of how LMs
bind and retrieve entities in-context.
Language models use positional, lexical, and reflexive mechanisms to bind and retrieve entities, with a causal model achieving high accuracy in predicting next tokens across various tasks and input lengths.
in-context reasoning language models entity binding positional mechanism lexical mechanism reflexive mechanism causal model next token distributions