The Whole Truth and Nothing But the Truth: Faithful and Controllable Dialogue Response Generation with Dataflow Transduction and Constrained Decoding

In a real-world dialogue system, generated responses must satisfy several interlocking constraints: being informative, truthful, and easy to control. The two predominant paradigms in language generation -- neural language modeling and rule-based generation -- both struggle to satisfy these constraints. Even the best neural models are prone to hallucination and omission of information, while existing formalisms for rule-based generation make it difficult to write grammars that are both flexible and fluent. We describe a hybrid architecture for dialogue response generation that combines the strengths of both approaches. This architecture has two components. First, a rule-based content selection model defined using a new formal framework called dataflow transduction, which uses declarative rules to transduce a dialogue agent's computations (represented as dataflow graphs) into context-free grammars representing the space of contextually acceptable responses. Second, a constrained decoding procedure that uses these grammars to constrain the output of a neural language model, which selects fluent utterances. The resulting system outperforms both rule-based and learned approaches in human evaluations of fluency, relevance, and truthfulness.

Tracing Knowledge in Language Models Back to the Training Data

Neural language models (LMs) have been shown to memorize a great deal of factual knowledge. But when an LM generates an assertion, it is often difficult to determine where it learned this information and whether it is true. In this paper, we introduce a new benchmark for fact tracing: tracing language models' assertions back to the training examples that provided evidence for those predictions. Prior work has suggested that dataset-level influence methods might offer an effective framework for tracing predictions back to training data. However, such methods have not been evaluated for fact tracing, and researchers primarily have studied them through qualitative analysis or as a data cleaning technique for classification/regression tasks. We present the first experiments that evaluate influence methods for fact tracing, using well-understood information retrieval (IR) metrics. We compare two popular families of influence methods -- gradient-based and embedding-based -- and show that neither can fact-trace reliably; indeed, both methods fail to outperform an IR baseline (BM25) that does not even access the LM. We explore why this occurs (e.g., gradient saturation) and demonstrate that existing influence methods must be improved significantly before they can reliably attribute factual predictions in LMs.

Identifying concept libraries from language about object structure

Our understanding of the visual world goes beyond naming objects, encompassing our ability to parse objects into meaningful parts, attributes, and relations. In this work, we leverage natural language descriptions for a diverse set of 2K procedurally generated objects to identify the parts people use and the principles leading these parts to be favored over others. We formalize our problem as search over a space of program libraries that contain different part concepts, using tools from machine translation to evaluate how well programs expressed in each library align to human language. By combining naturalistic language at scale with structured program representations, we discover a fundamental information-theoretic tradeoff governing the part concepts people name: people favor a lexicon that allows concise descriptions of each object, while also minimizing the size of the lexicon itself.

Correcting Robot Plans with Natural Language Feedback

When humans design cost or goal specifications for robots, they often produce specifications that are ambiguous, underspecified, or beyond planners' ability to solve. In these cases, corrections provide a valuable tool for human-in-the-loop robot control. Corrections might take the form of new goal specifications, new constraints (e.g. to avoid specific objects), or hints for planning algorithms (e.g. to visit specific waypoints). Existing correction methods (e.g. using a joystick or direct manipulation of an end effector) require full teleoperation or real-time interaction. In this paper, we explore natural language as an expressive and flexible tool for robot correction. We describe how to map from natural language sentences to transformations of cost functions. We show that these transformations enable users to correct goals, update robot motions to accommodate additional user preferences, and recover from planning errors. These corrections can be leveraged to get 81% and 93% success rates on tasks where the original planner failed, with either one or two language corrections. Our method makes it possible to compose multiple constraints and generalizes to unseen scenes, objects, and sentences in simulated environments and real-world environments.

Teachable Reinforcement Learning via Advice Distillation

Training automated agents to complete complex tasks in interactive environments is challenging: reinforcement learning requires careful hand-engineering of reward functions, imitation learning requires specialized infrastructure and access to a human expert, and learning from intermediate forms of supervision (like binary preferences) is time-consuming and extracts little information from each human intervention. Can we overcome these challenges by building agents that learn from rich, interactive feedback instead? We propose a new supervision paradigm for interactive learning based on "teachable" decision-making systems that learn from structured advice provided by an external teacher. We begin by formalizing a class of human-in-the-loop decision making problems in which multiple forms of teacher-provided advice are available to a learner. We then describe a simple learning algorithm for these problems that first learns to interpret advice, then learns from advice to complete tasks even in the absence of human supervision. In puzzle-solving, navigation, and locomotion domains, we show that agents that learn from advice can acquire new skills with significantly less human supervision than standard reinforcement learning algorithms and often less than imitation learning.

Pre-Trained Language Models for Interactive Decision-Making

Language model (LM) pre-training has proven useful for a wide variety of language processing tasks, but can such pre-training be leveraged for more general machine learning problems? We investigate the effectiveness of language modeling to scaffold learning and generalization in autonomous decision-making. We describe a framework for imitation learning in which goals and observations are represented as a sequence of embeddings, and translated into actions using a policy network initialized with a pre-trained transformer LM. We demonstrate that this framework enables effective combinatorial generalization across different environments, such as VirtualHome and BabyAI. In particular, for test tasks involving novel goals or novel scenes, initializing policies with language models improves task completion rates by 43.6% in VirtualHome. We hypothesize and investigate three possible factors underlying the effectiveness of LM-based policy initialization. We find that sequential representations (vs. fixed-dimensional feature vectors) and the LM objective (not just the transformer architecture) are both important for generalization. Surprisingly, however, the format of the policy inputs encoding (e.g. as a natural language string vs. an arbitrary sequential encoding) has little influence. Together, these results suggest that language modeling induces representations that are useful for modeling not just language, but also goals and plans; these representations can aid learning and generalization even outside of language processing.

Compositionality as Lexical Symmetry

Standard deep network models lack the inductive biases needed to generalize compositionally in tasks like semantic parsing, translation, and question answering. A large body of work in natural language processing seeks to overcome this limitation with new model architectures that enforce a compositional process of sentence interpretation. In this paper, we present a domain-general framework for compositional modeling that instead formulates compositionality as a constraint on data distributions. We prove that for any task factorizable into a lexicon and a composition function, there exists a family of data transformation functions that are guaranteed to produce new, well-formed examples when applied to training data. We further show that it is possible to identify these data transformations even when the composition function is unknown (e.g. when we do not know how to write or infer a symbolic grammar). Using these transformation functions to perform data augmentation for ordinary RNN and transformer sequence models, we obtain state-of-the-art results on the CLEVR-CoGenT visual question answering dataset, and results comparable to specialized model architectures on the COGS semantic parsing dataset.

Natural Language Descriptions of Deep Visual Features

Some neurons in deep networks specialize in recognizing highly specific perceptual, structural, or semantic features of inputs. In computer vision, techniques exist for identifying neurons that respond to individual concept categories like colors, textures, and object classes. But these techniques are limited in scope, labeling only a small subset of neurons and behaviors in any network. Is a richer characterization of neuron-level computation possible? We introduce a procedure (called MILAN, for mutual-information-guided linguistic annotation of neurons) that automatically labels neurons with open-ended, compositional, natural language descriptions. Given a neuron, MILAN generates a description by searching for a natural language string that maximizes pointwise mutual information with the image regions in which the neuron is active. MILAN produces fine-grained descriptions that capture categorical, relational, and logical structure in learned features. These descriptions obtain high agreement with human-generated feature descriptions across a diverse set of model architectures and tasks, and can aid in understanding and controlling learned models. We highlight three applications of natural language neuron descriptions. First, we use MILAN for analysis, characterizing the distribution and importance of neurons selective for attribute, category, and relational information in vision models. Second, we use MILAN for auditing, surfacing neurons sensitive to protected categories like race and gender in models trained on datasets intended to obscure these features. Finally, we use MILAN for editing, improving robustness in an image classifier by deleting neurons sensitive to text features spuriously correlated with class labels.

Modeling Strong and Human-Like Gameplay with KL-Regularized Search

We consider the task of building strong but human-like policies in multi-agent decision-making problems, given examples of human behavior. Imitation learning is effective at predicting human actions but may not match the strength of expert humans, while self-play learning and search techniques (e.g. AlphaZero) lead to strong performance but may produce policies that are difficult for humans to understand and coordinate with. We show in chess and Go that regularizing search policies based on the KL divergence from an imitation-learned policy by applying Monte Carlo tree search produces policies that have higher human prediction accuracy and are stronger than the imitation policy. We then introduce a novel regret minimization algorithm that is regularized based on the KL divergence from an imitation-learned policy, and show that applying this algorithm to no-press Diplomacy yields a policy that maintains the same human prediction accuracy as imitation learning while being substantially stronger.

Quantifying Adaptability in Pre-trained Language Models with 500 Tasks

When a neural language model (LM) is adapted to perform a new task, what aspects of the task predict the eventual performance of the model? In NLP, systematic features of LM generalization to individual examples are well characterized, but systematic aspects of LM adaptability to new tasks are not nearly as well understood. We present a large-scale empirical study of the features and limits of LM adaptability using a new benchmark, TaskBench500, built from 500 procedurally generated sequence modeling tasks. These tasks combine core aspects of language processing, including lexical semantics, sequence processing, memorization, logical reasoning, and world knowledge. Using TaskBench500, we evaluate three facets of adaptability, finding that: (1) adaptation procedures differ dramatically in their ability to memorize small datasets; (2) within a subset of task types, adaptation procedures exhibit compositional adaptability to complex tasks; and (3) failure to match training label distributions is explained by mismatches in the intrinsic difficulty of predicting individual labels. Our experiments show that adaptability to new tasks, like generalization to new examples, can be systematically described and understood, and we conclude with a discussion of additional aspects of adaptability that could be studied using the new benchmark.