5 accepted papers
Recent progress in language model reasoning capabilities has revived a classic goal: characterizing which algorithms such systems can implement, or equivalently, which formal languages they can recognize. A growing research program studies RNNs, LSTMs, SSMs, and related architectures via reductions …
Large language models (LLMs) trained on natural language data are capable of translating between languages, predict chess moves, and write poetry. Performance on a given task depends on directly relevant training data, yet confounders abound: data in related languages has been shown to help low-reso…
Modern language models define distributions over strings, but their outputs are not always suited to downstream task. For instance, a model generating byte-pair strings may not be suitable when word-level predictions are needed, and a DNA model may not fit applications requiring amino acids. In such…
Extracting finite state automata (FSAs) fromblack-box models offers a powerful approachto gaining interpretable insights into complexmodel behaviors. To support this pursuit, wepresent a weighted variant of Angluin’s (1987)L* algorithm for learning FSAs. We stay faithful to the original formulation,…
Pre-trained language encoders---functions that represent text as vectors---are an integral component of many NLP tasks. We tackle a natural question in language encoder analysis: What does it mean for two encoders to be similar? We contend that a faithful measure of similarity needs to be \e…