Singapore

Explanations for AI are expected to help human users understand AI-driven predictions. Evaluating plausibility, the helpfulness of the explanations, is therefore essential for developing eXplainable AI (XAI) that can really aid human users. Here we propose a human-centric evaluation platform1 to measure plausibility of explanations in the context of eXplainable Knowledge Graph Completion (XKGC). The target audience of the platform are researchers and practitioners who want to 1) investigate real needs and interests of their target users in XKGC, 2) evaluate the plausibility of XKGC methods.

EMNLP 2023

Human-Understandable Knowledge Graph Completion

explanable ai

knowledge graph completion

plausibility

workshop paper

**Welcome to EMNLP 2023!**

On behalf of the EMNLP 2023 Organizing Committee, I extend a warm and heartfelt welcome to all of you to the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP). It is with immense pleasure and excitement that we gather here in Singapore, a vibrant hub of innovation and technological advancement.

The conference program is packed with insightful presentations, thought-provoking workshops, and engaging networking opportunities. In addition to the technical sessions, we have also planned several social events that will provide you with the opportunity to connect with your colleagues.

The conference is held in person at the Resorts World Convention Centre in Singapore, and available on-line with the help of Underline.

For those in Singapore, I hope that you will find time to explore the exciting Sentosa Island, Gardens by the Bay, Singapore Botanic Gardens and the many other attractions unique to Singapore.

Of course, we are grateful to our sponsors and partners for their generous support of EMNLP 2023, whose contributions make it possible for us to host this world-class event.

Yuji Matsumoto (RIKEN AIP) 
EMNLP 2023 General Chair

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Please follow **[this link](https://2023.emnlp.org/registration/)** for more details.

EMNLP 2023 took place in Singapore from Dec 6th to Dec 10th, 2023.

Establishing whether language models can use contextual information in a human-plausible way is important to ensure their safe adoption in real-world settings. However, the questions of when and which parts of the context affect model generations are typically tackled separately, and current plausibility evaluations are practically limited to a handful of artificial benchmarks. To address this, we introduce Plausibility Evaluation of Context Reliance (PECoRe), an end-to-end interpretability framework designed to quantify context usage in language models' generations. Our approach leverages model internals to (i) contrastively identify context-sensitive target tokens in generated texts and (ii) link them to contextual cues justifying their prediction. We use PECoRe to quantify the plausibility of context-aware machine translation models, comparing model rationales with human annotations across several discourse-level phenomena. Finally, we apply our method to unannotated generations to identify context-mediated predictions and highlight instances of (im)plausible context usage in model translations.

Quantifying the Plausibility of Context Reliance in Neural Machine Translation

Enhancing Interpretability using Human Similarity Judgements to Prune Word Embeddings

The increasing interest in probing the linguistic capabilities of large language models (LLMs) has long reached the area of semantics and pragmatics, including the phenomenon of presuppositions. In this study, we investigate a phenomenon that, however, has not yet been investigated, i.e., the phenomenon of anti-presupposition and the principle that accounts for it, the Maximize Presupposition! principle (MP!). Through an experimental investigation using psycholinguistic data and four open-source BERT model variants, we explore how language models handle different antipresuppositions and whether they apply the MP! principle in their predictions. Further, we examine whether fine-tuning with Natural Language Inference data impacts adherence to the MP! principle. Our findings reveal that LLMs tend to replicate context-based n-grams rather than follow the MP! principle, with fine-tuning not enhancing their adherence. Notably, our results further indicate a striking difficulty of LLMs to correctly predict determiners, in relatively simple linguistic contexts.

When your Language Model cannot even do Determiners right: Probing for Anti-Presuppositions and the Maximize Presupposition! Principle

How do sequence models represent their decision-making process? Prior work suggests that Othello-playing neural network learned nonlinear models of the board state (Li et al., 2023a). In this work, we provide evidence of a closely related linear representation of the board. In particular, we show that probing for “my colour” vs. “opponent’s colour” may be a simple yet powerful way to interpret the model’s internal state. This precise understanding of the internal representations allows us to control the model’s behaviour with simple vector arithmetic. Linear representations enable significant interpretability progress, which we
demonstrate with further exploration of how the world model is computed.

Emergent Linear Representations in World Models of Self-Supervised Sequence Models

Explanations are meaningful only if it is understandable for human users. In daily life, humans explain to each other in natural language. However, eXplainable AI (XAI) identifies, e. g., important attributes from tabular data as explanations to a prediction, which are often difficult for humans to understand, especially when they
have no AI background. To solve the limitation, we aim to generate text-based comprehensive explanations conditioned on the important features selected by an XAI method. We expect the explanations can borrow strength from XAI and Large Language Models (LLMs), integrating the concrete information extracted by XAI from tabular data and the general domain knowledge retained in LLM from large corpus.

Natural Language Explanation for Tabular Data

We train a transformer network on the task of performing additions of two integers, achieving near perfect test-set accuracy with just 8000 positive training examples. We compute both learning curves (accuracy as a function of training set size) and model selection curves (accuracy as a function of embedding dimension and of number of layers). We observe that the number of dimension plays the biggest role, whereas the number of layers does not make a big difference after four layers. While the network generalises well from training to testing set, we did not observe any transfer-learning benefits when learning subtraction, having to relearn it from scratch. Current work involves probing the attention heads to understand the different roles played by positional embedding and the digit embedding (our conjecture is that query and key should be attending to position, while value should be attending to the actual digit). This distinction would not be possible when analysing words.

Learning to Add as a way to Understand Transformers

Examples are a powerful tool that help us understand complex concepts and connections. In computational linguistics research, looking at example system output and example corpus entries can offer a wealth of insights that are not otherwise accessible. This paper describes the open-source software VULCAN, a visualization tool for strings, graphs, trees, alignments, attention and more. 
VULCAN's unique ability to visualize both linguistic structures and properties of neural models make it particularly relevant for neuro-symbolic models. Neuro-symbolic models, combining neural networks with often linguistically grounded structures, offer a promise of increased interpretability in an age of purely neural black-box end-to-end models. VULCAN aims to facilitate this interpretability in practice.
VULCAN is designed to be both easy to use and powerful in its capabilities.

Introducing VULCAN: A Visualization Tool for Understanding our Models and Data by Example

Flexible Model Interpretability through Natural Language Model Editing

Several methodologies have recently been proposed to evaluate the ability of Pretrained Language Models (PLMs) to interpret negation. In this article, we build on Gubelmann and Hand- schuh (2022), which studies the modification of PLMs’ predictions as a function of the polarity of inputs, in English. Crucially, this test uses “self-contained” inputs ending with a masked position: depending on the polarity of a verb in the input, a particular token is either semantically ruled out or allowed at the masked position. By replicating Gubelmann and Handschuh (2022) experiments, we have uncovered flaws that weaken the conclusions that can be drawn from this test. We thus propose an improved version, the Self-Contained Neg Test, which is more controlled, more systematic, and entirely based on examples forming minimal pairs varying only in the presence or absence of verbal negation in English.
When applying our test to the roberta and bert base and large models, we show that only roberta-large shows trends that match the expectations, while bert-base is mostly insensitive to negation. For all the tested models though, in a significant number of test instances the top-1 prediction remains the token that is semantically forbidden by the context, which shows how much room for improvement remains for a proper treatment of the negation phenomenon.

Next from EMNLP 2023

Quantifying the Plausibility of Context Reliance in Neural Machine Translation

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