10 research outputs found
Large Language Model Augmented Narrative Driven Recommendations
Narrative-driven recommendation (NDR) presents an information access problem
where users solicit recommendations with verbose descriptions of their
preferences and context, for example, travelers soliciting recommendations for
points of interest while describing their likes/dislikes and travel
circumstances. These requests are increasingly important with the rise of
natural language-based conversational interfaces for search and recommendation
systems. However, NDR lacks abundant training data for models, and current
platforms commonly do not support these requests. Fortunately, classical
user-item interaction datasets contain rich textual data, e.g., reviews, which
often describe user preferences and context - this may be used to bootstrap
training for NDR models. In this work, we explore using large language models
(LLMs) for data augmentation to train NDR models. We use LLMs for authoring
synthetic narrative queries from user-item interactions with few-shot prompting
and train retrieval models for NDR on synthetic queries and user-item
interaction data. Our experiments demonstrate that this is an effective
strategy for training small-parameter retrieval models that outperform other
retrieval and LLM baselines for narrative-driven recommendation.Comment: Pre-prin
Multi-Vector Models with Textual Guidance for Fine-Grained Scientific Document Similarity
We present a new scientific document similarity model based on matching
fine-grained aspects of texts. To train our model, we exploit a
naturally-occurring source of supervision: sentences in the full-text of papers
that cite multiple papers together (co-citations). Such co-citations not only
reflect close paper relatedness, but also provide textual descriptions of how
the co-cited papers are related. This novel form of textual supervision is used
for learning to match aspects across papers. We develop multi-vector
representations where vectors correspond to sentence-level aspects of
documents, and present two methods for aspect matching: (1) A fast method that
only matches single aspects, and (2) a method that makes sparse multiple
matches with an Optimal Transport mechanism that computes an Earth Mover's
Distance between aspects. Our approach improves performance on document
similarity tasks in four datasets. Further, our fast single-match method
achieves competitive results, paving the way for applying fine-grained
similarity to large scientific corpora. Code, data, and models available at:
https://github.com/allenai/aspireComment: NAACL 2022 camera-read
Editable User Profiles for Controllable Text Recommendation
Methods for making high-quality recommendations often rely on learning latent
representations from interaction data. These methods, while performant, do not
provide ready mechanisms for users to control the recommendation they receive.
Our work tackles this problem by proposing LACE, a novel concept value
bottleneck model for controllable text recommendations. LACE represents each
user with a succinct set of human-readable concepts through retrieval given
user-interacted documents and learns personalized representations of the
concepts based on user documents. This concept based user profile is then
leveraged to make recommendations. The design of our model affords control over
the recommendations through a number of intuitive interactions with a
transparent user profile. We first establish the quality of recommendations
obtained from LACE in an offline evaluation on three recommendation tasks
spanning six datasets in warm-start, cold-start, and zero-shot setups. Next, we
validate the controllability of LACE under simulated user interactions.
Finally, we implement LACE in an interactive controllable recommender system
and conduct a user study to demonstrate that users are able to improve the
quality of recommendations they receive through interactions with an editable
user profile.Comment: Accepted to SIGIR 2023; Pre-print, camera-ready to follo
The Materials Science Procedural Text Corpus: Annotating Materials Synthesis Procedures with Shallow Semantic Structures
Materials science literature contains millions of materials synthesis
procedures described in unstructured natural language text. Large-scale
analysis of these synthesis procedures would facilitate deeper scientific
understanding of materials synthesis and enable automated synthesis planning.
Such analysis requires extracting structured representations of synthesis
procedures from the raw text as a first step. To facilitate the training and
evaluation of synthesis extraction models, we introduce a dataset of 230
synthesis procedures annotated by domain experts with labeled graphs that
express the semantics of the synthesis sentences. The nodes in this graph are
synthesis operations and their typed arguments, and labeled edges specify
relations between the nodes. We describe this new resource in detail and
highlight some specific challenges to annotating scientific text with shallow
semantic structure. We make the corpus available to the community to promote
further research and development of scientific information extraction systems.Comment: Accepted as a long paper at the Linguistic Annotation Workshop (LAW)
at ACL 201
Inorganic Materials Synthesis Planning with Literature-Trained Neural Networks
Leveraging new data sources is a key step in accelerating the pace of
materials design and discovery. To complement the strides in synthesis planning
driven by historical, experimental, and computed data, we present an automated
method for connecting scientific literature to synthesis insights. Starting
from natural language text, we apply word embeddings from language models,
which are fed into a named entity recognition model, upon which a conditional
variational autoencoder is trained to generate syntheses for arbitrary
materials. We show the potential of this technique by predicting precursors for
two perovskite materials, using only training data published over a decade
prior to their first reported syntheses. We demonstrate that the model learns
representations of materials corresponding to synthesis-related properties, and
that the model's behavior complements existing thermodynamic knowledge.
Finally, we apply the model to perform synthesizability screening for proposed
novel perovskite compounds.Comment: Added new funding support to the acknowledgments section in this
versio
PEARL: Personalizing Large Language Model Writing Assistants with Generation-Calibrated Retrievers
Powerful large language models have facilitated the development of writing
assistants that promise to significantly improve the quality and efficiency of
composition and communication. However, a barrier to effective assistance is
the lack of personalization in LLM outputs to the author's communication style
and specialized knowledge. In this paper, we address this challenge by
proposing PEARL, a retrieval-augmented LLM writing assistant personalized with
a generation-calibrated retriever. Our retriever is trained to select historic
user-authored documents for prompt augmentation, such that they are likely to
best personalize LLM generations for a user request. We propose two key
novelties for training our retriever: 1) A training data selection method that
identifies user requests likely to benefit from personalization and documents
that provide that benefit; and 2) A scale-calibrating KL-divergence objective
that ensures that our retriever closely tracks the benefit of a document for
personalized generation. We demonstrate the effectiveness of PEARL in
generating personalized workplace social media posts and Reddit comments.
Finally, we showcase the potential of a generation-calibrated retriever to
double as a performance predictor and further improve low-quality generations
via LLM chaining.Comment: Pre-print, work in progres
Inorganic Materials Synthesis Planning with Literature-Trained Neural Networks
Copyright © 2020 American Chemical Society. Leveraging new data sources is a key step in accelerating the pace of materials design and discovery. To complement the strides in synthesis planning driven by historical, experimental, and computed data, we present an automated, unsupervised method for connecting scientific literature to inorganic synthesis insights. Starting from the natural language text, we apply word embeddings from language models, which are fed into a named entity recognition model, upon which a conditional variational autoencoder is trained to generate syntheses for any inorganic materials of interest. We show the potential of this technique by predicting precursors for two perovskite materials, using only training data published over a decade prior to their first reported syntheses. We demonstrate that the model learns representations of materials corresponding to synthesis-related properties and that the model's behavior complements the existing thermodynamic knowledge. Finally, we apply the model to perform synthesizability screening for proposed novel perovskite compounds