United Arab Emirates

Language models suffer from various degenerate behaviors. These differ between tasks: machine translation (MT) exhibits length bias, while tasks like story generation exhibit excessive repetition. Recent work has attributed the difference to task constrainedness, but evidence for this claim has always involved many confounding variables. To study this question directly, we introduce a new experimental framework that allows us to smoothly vary task constrainedness, from MT at one end to fully open-ended generation at the other, while keeping all other aspects fixed. We find that: (1) repetition decreases smoothly with constrainedness, explaining the difference in repetition across tasks; (2) length bias surprisingly also decreases with constrainedness, suggesting some other cause for the difference in length bias; (3) across the board, these problems affect the mode, not the whole distribution; (4) the differences cannot be attributed to a change in the entropy of the distribution, since another method of changing the entropy, label smoothing, does not produce the same effect.

EMNLP 2022

A Continuum of Generation Tasks for Investigating Length Bias and Degenerate Repetition

length bias

repetition

generation

The purpose of the AAAI conference is to promote research in artificial intelligence (AI) and scientific exchange among AI researchers, practitioners, scientists, and engineers in affiliated disciplines. 

AAAI 2021

workshop paper

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EMNLP 2022 will take place in Abu Dhabi from December 7th to December 11th, 2022. And it will be held in hybrid mode, both online and offline.

Linguistic analysis of language models is one of the ways to explain and describe their reasoning, weaknesses, and limitations. 
In the probing part of the model interpretability research, studies concern individual languages as well as individual linguistic structures. 
The question arises: are the detected regularities linguistically coherent, or on the contrary, do they dissonate at the typological scale? Moreover, the majority of studies address the inherent set of languages and linguistic structures, leaving the actual typological diversity knowledge out of scope.
In this paper, we present and apply the GUI-assisted framework allowing us to easily probe massive amounts of languages for all the morphosyntactic features present in the Universal Dependencies data. We show that reflecting the anglo-centric trend in NLP over the past years, most of the regularities revealed in the mBERT model are typical for the western-European languages. 
Our framework can be integrated with the existing probing toolboxes, model cards, and leaderboards, allowing practitioners to use and share their familiar probing methods to interpret multilingual models.
Thus we propose a toolkit to systematize the multilingual flaws in multilingual models, providing a reproducible experimental setup for 104 languages and 80 morphosyntactic features.

Universal and Independent: Multilingual Probing Framework for Exhaustive Model Interpretation and Evaluation

We study whether linguistic knowledge is a necessary condition for the good performance of modern language models, which we call the rediscovery hypothesis. First, we show that language models that are significantly compressed but perform well on their pretraining objectives retain good scores when probed for linguistic structures. This result supports the rediscovery hypothesis and leads to the second contribution of our paper: an information-theoretic framework that relates language modeling objectives with linguistic information.

The Rediscovery Hypothesis: Language Models Need to Meet Linguistics

In this paper, we set out to quantify the syntactic capacity of BERT in the evaluation regime of non-context free patterns, as occurring in Dutch. We devise a test suite based on a mildly context-sensitive formalism, from which we derive grammars that capture the linguistic phenomena of control verb nesting and verb raising. The grammars, paired with a small lexicon, provide us with a large collection of naturalistic utterances, annotated with verb-subject pairings, that serve as the evaluation test bed for an attention-based span selection probe. Our results, backed by extensive analysis, suggest that the models investigated fail in the implicit acquisition of the dependencies examined.

Discontinuous Constituency and BERT: A Case Study of Dutch

Garden path sentences (i.e. “the horse raced past the barn fell”) are sentences that readers initially incorrectly parse, requiring partial or total re-analysis of the sentence structure. Given human difficulty in parsing garden paths, we aim to compare transformer language models' performance on these sentences. We assess a BERT question-answering model's performance on comprehension questions based on garden path and control sentences. We then further investigate the semantic roles assigned to arguments of verbs in garden path and control sentences by utilizing a probe task to directly assess which semantic role(s) BERT assigns. We find that BERT has relatively low performance in question answering based on garden path contexts, and the model incorrectly assigns semantic roles, aligning for the most part with human performance.

The BERT Walked Down the Garden Path Assigned Semantic Roles

Understanding Transformer-based models has attracted significant attention, as they lie at the heart of recent technological advances across machine learning.
While most interpretability methods rely on running models over inputs, recent work has shown that a zero-pass approach, where parameters are interpreted directly without a forward/backward pass is feasible for \emph{some} Transformer parameters, and for two-layer attention networks.
In this work, we present a theoretical analysis where \emph{all} parameters of a trained Transformer are interpreted by projecting them into the \emph{embedding space}, that is, the space of vocabulary items they operate on.
We derive a simple theoretical framework to support our arguments and provide ample evidence for its validity. First, an empirical analysis showing that parameters of both pretrained and fine-tuned models can be interpreted in embedding space. Second, we present two applications of our framework: (a) aligning the parameters of different models that share a vocabulary, and (b) constructing a classifier \emph{without training} by
``translating'' the parameters of a fine-tuned classifier to parameters of a different model that was only pretrained. Overall, our findings open the door 
to interpretation methods that, at least in part, abstract away from model specifics and operate in the embedding space only.

Analyzing Transformers in Embedding Space

We study feature interactions in the context of feature attribution methods for post-hoc interpretability. 
Feature interactions are a mixed blessing: on the one hand, the ability to deal with interacting features is  seen as key to the success of neural networks in modern AI, allowing a model to build up hierarchical representations for its input. 
On the other hand, uncovering what exact role these interactions play in the decision making process of a model is challenging, leading to a diverse range of interaction attribution methods being proposed. 
In this paper, we introduce a unified framework that allows for direct comparison of these varied methods. Our framework is based on the `Explaining by Removing' framework of Covert et al. (2021) for feature attributions, and generalises it to feature interactions. To evaluate the faithfulness of methods for detecting feature interactions, we work out a grey box methodology, where we train deep learning models to perfection on a formal language classification task, using PCFGs. We show that under specific configurations, some interaction attribution methods are indeed able to uncover the grammatical rules acquired by a model. Our framework, and empirical results, thus provide a sound basis for comparing and extending methods for detecting feature interactions.

FIDAM-Eval: A Framework for Evaluating Feature Interaction Detection and Attribution Methods

The gold standard for model explanation should be analysis of the causal effects of model components on model behavior. We argue that causal abstraction provides a powerful toolkit for achieving such explanations.

Faithful, Interpretable Model Explanations via Causal Abstraction

There are numerous applications for the prediction of new links within knowledge graphs. The best models for this task, knowledge graph embedding (KGE) models, are typically evaluated by computing some average performance metric on a test set. This approach makes it difficult to identify where the models might systematically fail. To combat this issue, we propose a new evaluation framework that builds upon the idea of behavioral testing, allowing us to target critical system capabilities and evaluate the performance of KGE models on them.

Behavioral Testing of Knowledge Graph Embedding Models for Link Prediction

LSTMs trained on next word prediction can accurately perform linguistic tasks that require tracking long-distance syntactic dependencies. Notably, model accuracy approaches human performance on number agreement tasks (Gulordava et al., 2018). However, we do not have a mechanistic understanding of how LSTMs perform such linguistic tasks. Do LSTMs learn abstract grammatical rules, or do they rely on simple heuristics? Here, we test gender agreement in French which requires tracking both hierarchical syntactic structures and the inherent gender of lexical units. Our model is able to reliably predict long-distance gender agreement in two subject-predicate contexts: noun-adjective and noun-passive-verb agreement. The model showed more inaccuracies on plural noun phrases with gender attractors compared to singular cases, suggesting a reliance on clues from gendered articles for agreement. Overall, our study highlights key ways in which LSTMs deviate from human behaviour and questions whether LSTMs genuinely learn abstract syntactic rules and categories. We propose using gender agreement as a useful probe to investigate the underlying mechanisms, internal representations, and linguistic capabilities of LSTM language models.

Do LSTMs See Gender? Probing the Ability of LSTMs to Learn Abstract Syntactic Rules

Following how humans communicate, free-text rationales aim to use natural language to explain neural language model (LM) behavior. However, free-text rationales' unconstrained nature makes them prone to hallucination, so it is important to have metrics for free-text rationale quality. Existing free-text rationale metrics measure how consistent the rationale is with the LM's predicted label, but there is no protocol for assessing such metrics' reliability. Thus, we propose FRAME, a framework for evaluating rationale-label consistency (RLC) metrics for free-text rationales. FRAME is based on three axioms: (1) good metrics should yield highest scores for reference rationales, which maximize RLC by construction; (2) good metrics should be appropriately sensitive to semantic perturbation of rationales; and (3) good metrics should be robust to variation in the LM's task performance. Across three text classification datasets, we show that existing RLC metrics cannot satisfy all three FRAME axioms, since they are implemented via model pretraining which muddles the metric's signal. Then, we introduce a non-pretraining RLC metric that greatly outperforms baselines on (1) and (3), while performing competitively on (2). Finally, we discuss the limitations of using RLC to evaluate free-text rationales.

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