How Language Model Hallucinations Can Snowball

A major risk of using language models in practical applications is their tendency to hallucinate incorrect statements. Hallucinations are often attributed to knowledge gaps in LMs, but we hypothesize that in some cases, when justifying previously generated hallucinations, LMs output false claims that they can separately recognize as incorrect. We construct three question-answering datasets where ChatGPT and GPT-4 often state an incorrect answer and offer an explanation with at least one incorrect claim. Crucially, we find that ChatGPT and GPT-4 can identify 67% and 87% of their own mistakes, respectively. We refer to this phenomenon as hallucination snowballing: an LM over-commits to early mistakes, leading to more mistakes that it otherwise would not make

Measuring and Narrowing the Compositionality Gap in Language Models

We investigate the ability of language models to perform compositional reasoning tasks where the overall solution depends on correctly composing the answers to sub-problems. We measure how often models can correctly answer all sub-problems but not generate the overall solution, a ratio we call the compositionality gap. We evaluate this ratio by asking multi-hop questions with answers that require composing multiple facts unlikely to have been observed together during pretraining. In the GPT-3 family of models, as model size increases we show that the single-hop question answering performance improves faster than the multi-hop performance does, therefore the compositionality gap does not decrease. This surprising result suggests that while more powerful models memorize and recall more factual knowledge, they show no corresponding improvement in their ability to perform this kind of compositional reasoning. We then demonstrate how elicitive prompting (such as chain of thought) narrows the compositionality gap by reasoning explicitly instead of implicitly. We present a new method, self-ask, that further improves on chain of thought. In our method, the model explicitly asks itself (and then answers) follow-up questions before answering the initial question. We finally show that self-ask's structured prompting lets us easily plug in a search engine to answer the follow-up questions, which additionally improves accuracy

Advances in Conjugated Polymer Lasers

This paper provides a review of advances in conjugated polymer lasers. High photoluminescence efficiencies and large stimulated emission cross-sections coupled with wavelength tunability and low-cost manufacturing processes make conjugated polymers ideal laser gain materials. In recent years, conjugated polymer lasers have become an attractive research direction in the field of organic lasers and numerous breakthroughs based on conjugated polymer lasers have been made in the last decade. This paper summarizes the recent progress of the subject of laser processes employing conjugated polymers, with a focus on the photoluminescence principle and excitation radiation mechanism of conjugated polymers. Furthermore, the effect of conjugated polymer structures on the laser threshold is discussed. The most common polymer laser materials are also introduced in detail. Apart from photo-pumped conjugated polymer lasers, a direction for the future development of electro-pumped conjugated polymer lasers is proposed

Biomimetic Human Serum Albumin Nanoparticle for Efficiently Targeting Therapy to Metastatic Breast Cancers

Triple-negative breast cancers (TNBCs), devoid of hormone receptors and human epidermal growth-factor receptor-2/Neu expression, bring about poor prognosis and induce a high rate of systematic metastases. The ineffectiveness of current therapies on TNBCs could be attributed to the lack of efficient targeted therapy. Paclitaxel (PTX) is considered one of first-line chemotherapeutics for TNBC treatment but, due to its low aqueous solubility and nonspecific accumulation, results in poor antitumor efficacy. The present study is aimed at enhancing the chemotherapeutic potency of PTX by improving the stability and targeting efficiency of PTX-loaded nanoparticulate drug carriers. Here, PTX was incorporated in nontoxic and endogenous material, human serum albumin (HSA), via an innovative disulfide reduction method to construct HSA-based PTX nanoparticle (HSA-PTX NP) to not only realize redox-responsive drug release but also improve in vivo stability. Besides, W peptide was selected as a target ligand to be conjugated with HSA-PTX NP for endowing active targeting ability. The resulting Wpep-HSA-PTX NP possessed a spherical structure (118 nm), 9.87% drug-loading content, and 86.3% entrapment efficiency. An in vitro drug release test showed that PTX release from Wpep-HSA-PTX NP was of a redox-responsive manner. Furthermore, cellular uptake of Wpep-HSA-PTX NP was significantly enhanced, exhibiting the improved antiproliferation and antitube formation effects of PTX in vitro. In comparison with those commercial formulations and conventional HSA NP, Wpep-HSA-PTX NP exhibited better pharmacokinetic behaviors and tumor homing characteristics. The antitumor efficacy of Wpep-HSA-PTX NP was further confirmed by the strong pro-apoptotic effect and reduced tumor burden. In a word, this evidence highlighted the proof of concept for Wpep-HSA NP as a promising conqueror to the ineffectiveness of TNBC therapy